Metaheuristic algorithms, as effective methods for solving optimization problems, have recently attracted considerable attention in science and engineering fields. They are popular and have broad applications owing to their high efficiency and low complexity. These algorithms are generally based on the behaviors observed in nature, physical sciences, or humans. This study proposes a novel metaheuristic algorithm called dark forest algorithm (DFA), which can yield improved optimization results for global optimization problems. In DFA, the population is divided into four groups: highest civilization, advanced civilization, normal civilization, and low civilization. Each civilization has a unique way of iteration. To verify DFA’s capability, the performance of DFA on 35 well-known benchmark functions is compared with that of six other metaheuristic algorithms, including artificial bee colony algorithm, firefly algorithm, grey wolf optimizer, harmony search algorithm, grasshopper optimization algorithm, and whale optimization algorithm. The results show that DFA provides solutions with improved efficiency for problems with low dimensions and outperforms most other algorithms when solving high dimensional problems. DFA is applied to five engineering projects to demonstrate its applicability. The results show that the performance of DFA is competitive to that of current well-known metaheuristic algorithms. Finally, potential upgrading routes for DFA are proposed as possible future developments.
With the continuing development of modern industry, optimization strategies are becoming increasingly essential; therefore, novel optimization algorithms need to be developed. To date, numerous optimization algorithms have been proposed, with nature-inspired algorithms being some of the most successful algorithms. Compared to traditional optimization methods, such as the gradient descent and direct search methods [1,2], metaheuristic algorithms have the following advantages: 1) Simplicity: The process and the procedural implementation of metaheuristic algorithms are often relatively straightforward. 2) Flexibility: Metaheuristic algorithms can be applied to many different fields. 3) Avoiding local optima: Metaheuristic algorithms aim to search beyond the local optima for the global optima of the target solutions or to directly find global optima. 4) Free of derivatives: Derivatives are generally expensive or difficult to evaluate within complex systems, and metaheuristic algorithms usually do not employ gradients for optimization. 5) Fast Processing: Most metaheuristic algorithms perform quick searches in the sample space to obtain a suitable solution.
Metaheuristic algorithms are often inspired by observations from natural phenomena. For example, the ant colony optimization (ACO) algorithm is inspired by the foraging behavior of ants [3]; the particle swarm optimization (PSO) algorithm mimics the predatory behavior of birds [4]; the genetic algorithm (GA) models the crossover variation of DNA [5]; the simulated annealing algorithm simulates the annealing technology in materials physics [6]. Algorithm designs aim not to mimic the behavior of an individual or population but rather to derive an algorithm to solve specific problems. Thus, versatile and intelligent variants could be introduced into metaheuristic algorithms to solve various optimization problems. In fact, metaheuristic algorithms are widely used in industrial applications, including solar energy forecasting, demand and supply analysis optimization in food manufacturing industry [7–9].
Most metaheuristic algorithms are characterized by their randomness, communication, exploration, and exploitation. Randomness provides algorithms with the possibility of achieving superior optimization solutions. Communication renders information exchange between individual solutions and thereby enables their learning from each other to yield superior optimization solutions. Exploration provides algorithms with a trail of new ideas or strategies, while exploitation allows algorithms to adopt the techniques that have proven successful in the past [10].
In this study, a new algorithm called dark forest algorithm (DFA) is proposed based on the rule of superiority and inferiority among natural civilizations and the universe’s dark forest law [11]. Then, the performance of DFA is compared with that of other well-known metaheuristic algorithms according to 35 benchmark functions and five engineering projects. The 35 benchmark functions comprise single-peaked, multi-peaked, high-dimensional, and fixed-dimensional functions. The results show that DFA can obtain qualified optimization results and it outperforms the compared algorithms in terms of overall performance.
The rest of this paper is organized as follows. Section 2 introduces the literature related to the development and applications of metaheuristic algorithms. Section 3 describes the mathematical model of the proposed DFA and the workflow and pseudo-code of the algorithm. Section 4 compares the performance of DFA with six other metaheuristic algorithms on 35 benchmark functions. Section 5 illustrates three engineering design problems using DFA with discussions on their outcomes. Finally, Section 6 presents the conclusions and suggestions for future research.
Related Works
Many intricate and fascinating phenomena can be observed in nature that provide inspiration for solving practical problems. The known metaheuristic algorithms can be classified into five categories according to the sources of their inspiration: evolutionary algorithms, swarm intelligence algorithms, physics-based algorithms, human-based algorithms, and other algorithms (Fig. 1).
Classification of metaheuristic algorithms
Evolutionary algorithms are based on natural evolution and are the first metaheuristic algorithms proposed. They are the most commonly used. They are based on phenomena and developed theories within biological evolution. Typical algorithms in this category include GA, evolutionary programming [12], differential evolution algorithms [13], evolution strategy [14], flower pollination algorithm [15], and harmony search algorithm (HS) [16]. Among them, GA is the earliest, best known, and one of the most widely used evolutionary algorithms. To date, GA has solved many problems, such as carpool service problems in cloud computing [17], image enhancement and segmentation [18], and routing problems with lost sales [19].
Swarm intelligence algorithms are constructed by simulating group activities inspired by swarms in nature, such as coenosis or social animals. The so-called group intelligence includes the behaviors of simple individuals and the whole group, exhibiting a specific intelligent feature without centralized control. Typical examples include ACO algorithm, PSO algorithm, artificial bee colony algorithm (ABC) [20], artificial fish swarming algorithm [21], grey wolf optimizer (GWO) [22], firefly algorithm (FA) [23], cuckoo search algorithm [24], chimp optimization algorithm [25], grasshopper optimization algorithm (GOA) [26], slime mold algorithm [27], and whale optimization algorithm (WOA) [28].
Physics-based algorithms are inspired by the physics of matter, and they usually have a solid theoretical basis. Some popular algorithms in this category are SA, central force optimization [29], gravitational search algorithm [30], water cycle algorithm [31], atom search optimization [32], electromagnetic field optimization [33], Henry gas solubility optimization [34], and wind driven optimization [35].
Human-based algorithms are designed based on human activities in the society. For example, teaching-learning-based optimization (TLBO) is inspired by the interaction between a learner’s learning and a teacher’s teaching [36]. TLBO simulates the traditional classroom teaching process. The entire optimization process includes the teacher and learner sections. At the teacher level, each student learns from the best individuals. During the learning section, each student randomly learns from the other students. Examples of popular human-based algorithms are brain storm optimization [37], imperialist competitive algorithm [38], cultural algorithm [39], coronavirus herd immunity optimization [40], and team competition and cooperation optimization algorithm [41].
Some algorithms do not fall into any of the above categories but are inspired by other natural phenomena, such as water drops algorithm [42], artificial ecosystem-based optimization [43], and invasive weed colony optimization [44].
Although many metaheuristic algorithms already exist, new algorithms still need to be designed. According to the No Free Lunch (NFL) theorem [45], no single metaheuristic algorithm can accurately solve all optimization problems. In practice, some algorithms perform better than others in specific situations, and many researchers are trying to find ways to improve existing metaheuristic algorithms or develop new ones. This study proposes a novel algorithm, i.e., DFA. Rigorous experiments are conducted to demonstrate DFA as a feasible metaheuristic algorithm that can be readily used in engineering problems.
Dark Forest AlgorithmInspiration
Civilizations in the universe survive under the dark forest law. According to the dark forest law, a civilization once discovered will inevitably be attacked by other civilizations in the universe. The universe’s evolution is endless and is always accompanied by the extinction of existing civilizations and the birth of new ones. Civilizations plunder each other and cooperate, constantly moving toward a better direction. Civilizations can be classified according to their level of development: highest, advanced, normal, and low civilizations.
Each civilization has its own exploration strategy. The highest civilizations cannot learn from others because no known civilizations are superior to them, and they usually move around during exploration. The highest civilizations sometimes plunder other civilizations for their development. If the highest civilization does not evolve, they remain unchanged. Advanced civilizations learn from the highest civilizations and plunder the normal civilizations. Normal civilizations learn from the highest and advanced civilizations, and they choose which civilizations to learn from based on the strengths and weaknesses of the other civilizations and their distance. Finally, the low civilizations are subject to elimination, at which point newly created civilizations enter the iterations.
Algorithm
This section describes the mathematical model, algorithmic workflow, and pseudo-code of DFA. The general workflow of the algorithm is as follows: 1) randomly initializing the population coordinates in the solution space; 2) classifying civilizations according to the adaptability of each civilization coordinate; 3) iteratively updating all locations according to the corresponding civilization level; and 4) separately performing a refined search for the highest civilization in the last few iterations. Fig. 2 illustrates the flowchart.
Flowchart of the proposed DFA
Population Initialization
DFA is a population-based algorithm. Similar to other population-based metaheuristic algorithms, it generates several uniformly distributed initial solutions in the search space at the beginning as follows:
xij=xi,minj+α⋅(xi,maxj−xi,minj)Xi=[xi1,xi2,…,xid]where xij is the initial value of the jth component of the ith solution, xi,minj is the minimum value allowed for the jth component of the candidate solution, xi,maxj is the maximum value allowed for the j th component of the candidate solution, and α is a uniformly distributed random number in the range of [0, 1]. These components form the corresponding initial solution Xi, and d is the dimension of the solution. Xi corresponds to the degree of adaptation; f(Xi) is evaluated according to the fitness function as follows:
f(Xi)=f([xi1,xi2,…,xid])
All solutions are constructed into a matrix X:
X=[X1X2⋮Xi⋮Xn]=[x11x12…x1j…x1dx21x22…x2j…x2d⋮⋮⋮⋮⋮⋮xi1xi2…xij…xid⋮⋮⋮⋮⋮⋮xn1xn2…xnj…xnd],{i=1,2,…,nj=1,2,…,dwhere n is the number of populations, i is an integer in the range of [1,n], and j is an integer in the range of [1,d].
Civilization Location Update
After the initial population is generated, the population is sorted according to the fitness values of each civilization. The top-ranked civilization is the highest civilization; the civilizations with the top 3/10 of the population, except for the highest civilization, are the advanced civilizations. The civilizations with ranking between 3/10 and 8/10 are defined as the normal civilizations, and the remaining civilizations are defined as low civilizations. Different civilization types update their coordinates in different ways.
Locations of the highest civilizations are updated as follows. The highest civilization has globally optimal fitness, and its primary purpose of movement is to find coordinates with enhanced fitness. During the iteration process, the highest civilization only changes its coordinates when it finds improved fitness. The highest civilization moves randomly in 50% of the cases and takes reference from the advanced civilization for the other 50% of the cases. The update formulas are as follows:
Xi,t+1={Xnew,f(Xnew)<f(Xi,t+1)Xi,t,otherwiseXnew={Xtry,p<0.5Xi,t+r⋅step⋅U,otherwiseXtryj={Xi,tj,p<0.5Xs,tj,otherwisestep=(1−tMax_Iter)2tMax_Iterwhere t is the current index of iteration, and p and r are random numbers in the range of [0,1]. U is a randomly generated vector with the same dimension as Xi and elements in the range of [−1,1], and step is the step size that decreases with increasing number of iterations, allowing civilizations to have exploration capability at the beginning and a more vital exploitation ability at the end. Xs,t is a randomly selected advanced civilization, and Max_Iter is the maximum number of iterations.
Locations of the advanced civilizations are updated as follows. Advanced civilizations obtain reference from normal civilizations for 20% of the cases. Since the highest civilization has better fitness than the advanced civilization, advanced civilizations that move to the highest civilizations are more likely to obtain superior results. Hence, the probability for advanced civilizations to move to the highest civilization is set as 80%. Advanced civilizations use spiral location updates in most cases, similar to the spiral update in WOA, but with variations. Mathematically, the update formulas are as follows:
Xi,t+1={Xtry,p<0.2Xi,t+D⋅ebl⋅cos(2πl),otherwiseXtryj={Xi,tjj,p<0.5Xc,tj,otherwiseD=|X∗−Xi,t|where b is a logarithmic spiral shape constant, l is a random number in the range of [−1, 1], Xc,t is a randomly selected normal civilization, and X∗ is the coordinate of a highest civilization.
Locations of normal civilizations are updated as follows. Normal civilizations also employ spiral position update. The reference is a civilization coordinate obtained via linear ranking and roulette selection, which may be either the highest civilization or an advanced civilization. In the selection process, the probability of each civilization is jointly determined by its fitness and its distance from the current normal civilization, with the weight ratio of fitness to distance length of 4:1. The update formulas are
Xi,t+1=Xi,t+D⋅ebl⋅cos(2πl)D=|Xm,t−Xi,t|where Xm,t is a civilization coordinate obtained by linear ranking and roulette selection.
Locations of low civilizations are updated as follows. Due to the poor adaptation of the low civilizations themselves, the coordinates of the low civilizations are reset for 50% of the cases. This is reasonable as the primary responsibility of a low civilization is to maintain the population diversity to ensure that it does not fall into a local optimum. A new coordinate is updated by mapping the coordinate centroid of mass of all the highest and advanced civilizations for 50% of the cases. The update formulas are
Xi,t+1={Xinit,p<0.5Xw+δ⋅(Xw−Xi,t)⋅step,otherwiseXw=1N∑1NXiwhere Xinit is a coordinate re-generated at random using Eq. (1), Xw is the generated centroid of mass of the highest and advanced civilizations, δ is the mapping factor with values in the range of [0, 1], and N is the sum of the number of highest and advanced civilizations.
After all civilization locations are updated, one refined search in the last ten iterations is performed for the highest civilizations. The refined search is performed to update each component Xi,t+1 of the coordinates of the highest civilization. At the beginning of an initial update vector β, the fitness of Xi,t+1 plus βi is calculated corresponding to Xnew. If no better fitness is obtained, this update is abandoned and the minus value of βi is taken. If a better fitness is obtained, then βi is kept unchanged. If no better result is obtained in two consecutive updates of βi, then βi is inverted and reduced by half. The refined search process only updates the highest civilizations for better optimization results. The above stated process can be represented as
Xi,t+1={Xnew,f(Xnew)<f(Xi,t+1)Xi,t,otherwiseXnewj=Xi,tj+(−1)k⋅βi⌈k2⌉k={k,f(xnew)<f(xi,t+1)(k+1)mod2,otherwisewhere β is the search radius typically in the range of [0, 1] and the value of k is initially 1.
Termination
The algorithm ends after a specified number of iterations is reached, with the coordinates recorded by the highest civilization at the end being the optimal solution and the corresponding fitness being the optimal fitness. The pseudo-code of DFA algorithm is presented in Table 1.
DFA pseudo-code
Algorithm
Input: population Xi(i=1,2,…,n)
Output: the best solution Ybest
1: procedure DFA2: Initialize parameters b, δ, and β
3: Compute the fitness of each solution
4: whileiter < Max number of iterations − 10 do5: Ranking of solutions according to fitness6: Update Ybest if there is a better solution than previous best solution7: for highest civilization do
8: Update the position by Eqs. (5)–(8)
9: end for
10: for each advanced civilization do
11: Update the position by Eqs. (9)–(11)
13: for each normal civilization do
14: Update the position by Eqs. (12), (13)
15: end for
16: for each low civilization do
17: Update the position by Eq. (1) and Eqs. (14), (15)
18: end for
19: Check if any search solution goes beyond the given search space and then adjust it
20: Compute the fitness of each solution
21: iter=iter+1
22: end while
23: Refine search of best solution by Eqs. (16)–(18)24: return Ybest25: end procedure
Results and Discussion
In this section, the results of DFA on 35 benchmark functions are shown. Table 2 presents these 35 benchmark functions, and Fig. 3 displays the 3D representations of some benchmark functions. Among them, F1–F6 are low-dimensional single-peaked functions; F7–F13 are low-dimensional multi-peaked functions; F14–F20 are high-dimensional single-peaked functions; F21–F29 are high-dimensional multi-peaked functions; and F30–F35 are fixed-dimensional functions. The performance of DFA on these functions is compared with that of six well-known algorithms: ABC, FA, GWO, HS, GOA, and WOA.
For the abovementioned metaheuristic algorithms, the same initialization process as DFA is employed. To reduce the effect of randomness on the test results, all algorithms on the benchmark function are executed in 30 independent runs with 500 iterations per run. The average values and standard deviations are obtained after the evaluation of the algorithms’ performance.
Evaluation of Exploitation Capability
Benchmark functions F1–F6 are single-peaked functions since they have only one global optimum. They are mainly used to assess the exploitation capability of metaheuristic algorithms. Table 3 shows the optimization results of DFA and other algorithms. The table shows that DFA yields better optimization results than the other algorithms, indicating that DFA has the best exploitation capability.
Optimization results of low-dimensional single-peaked functions
DFA
ABC
FA
GWO
HS
GOA
WOA
F1
Mean
3.00
3.016132
3.000004
3.000028
8.400001
3.00
3.00
Std.
1.30E−15
0.026696
7.94E−06
5.48E−05
11.16211
1.26E−12
5.11E−15
F2
Mean
0.397887
0.398291
0.397888
0.397889
0.397887
0.397887
0.397887
Std.
4.30E−16
5.59E−4
1.85E−06
5.42E−06
7.89E−10
2.77E−14
1.16E−15
F3
Mean
0.00
0.231584
2.37E−05
0.00
5.01E−05
2.36E−11
0.00
Std.
0.00
0.2265062
5.45E−05
0.00
7.89E−05
2.73E−11
0.00
F4
Mean
−1.00
−0.99941
−1.00
−1.00
0.9667
−1.00
−1.00
Std.
0.00
1.20E−03
9.68E−07
1.29E−05
2.42E−01
2.63E−13
3.93E−17
F5
Mean
0.00
0.00041
1.37E−07
0.101609
0.065531
0.127012
0.00
Std.
0.00
7.54E−04
3.05E−07
2.77E−01
2.38E−01
2.97E−01
0.00
F6
Mean
1.00
3.478734
4370.4147
1.00
1.116218
1.00
1.00
Std.
0.00
3.964094
6136.6486
0.00
1.13E−01
1.78E−06
7.85E−17
Benchmark functions F7–F13 are multi-peaked functions that have many local optima. The multimodal function can well detect the exploration ability of metaheuristic algorithms. Poor performing metaheuristic algorithms can be easily trapped in the local optimal values in the function and thus will not yield global optimization results. Table 4 shows that DFA yields optimal results for six benchmark functions, proving that it has a good exploration capability.
Optimization results of low-dimensional multi-peaked functions
DFA
ABC
FA
GWO
HS
GOA
WOA
F7
Mean
0.998004
0.999009
0.998004
2.967961
0.998004
1.031138
1.527099
Std.
2.78E−17
2.57E−03
7.14E−07
2.918416
3.90E−11
2.41E−01
9.55E−01
F8
Mean
−1.031628
−1.030659
−1.031628
−1.031628
−1.031628
−1.031628
−1.031628
Std.
5.81E−16
1.56E−03
894E−07
1.87E−08
1.44E−09
2.36E−13
4.68E−16
F9
Mean
−1.801303
−1.800267
−1.801303
−1.801301
−1.801303
−1.755019
−1.715869
Std.
8.90E−16
1.47E−03
2.46E−06
6.03E−06
2.00E−10
2.15E−01
2.48E−01
F10
Mean
0.002456
0.003090
0.002456
0.002456
0.014192
0.002456
0.009323
Std.
0.00
4.60E−03
2.27E−08
2.69E−08
1.93E−02
4.66E−14
1.44E−02
F11
Mean
−0.997875
−0.971810
−0.999995
−0.980874
−0.905380
−1.00
−0.970248
Std.
1.54E−02
2.80E−02
1.55E−05
2.96E−02
7.10E−02
1.35E−12
3.18E−02
F12
Mean
−186.7309
−186.6006
−186.7124
−186.7198
−186.7309
−186.7309
−186.7309
Std.
2.13E−14
2.63E−01
4.44E−02
4.32E−02
1.48E−05
5.71E−10
3.26E−14
F13
Mean
−106.7645
−106.7416
−106.7645
−106.7645
−106.7645
−104.1707
−106.1161
Std.
4.63E−14
2.99E−02
2.33E−04
1.73E−04
1.70E−07
7.06E+00
4.71E+00
Compared to F1–F6, benchmark functions F14–FF20 have increased dimensionality from 2 to 30 dimensions, and thus, their difficulty of exploitation is dramatically higher. Table 5 shows that DFA is highly competitive with other metaheuristic algorithms and yields the best or second best optimization results for most of the benchmark functions.
Optimization results of high-dimensional single-peaked functions
DFA
ABC
FA
GWO
HS
GOA
WOA
F14
Mean
0.092286
473.31073
45712.240
3.06E−84
0.541061
6204.1981
3.01E−98
Std.
5.96E−02
6.12E+02
1.07E+04
1.69E−83
3.82E−01
3.59E+03
1.55E−97
F15
Mean
0.461559
2.681364
1.69E+12
1.54E−54
0.197023
69.192744
5.36E−61
Std.
3.67E−01
1.97E+00
8.32E+12
4.56E−54
1.07E−01
3.66E+01
3.63E−60
F16
Mean
289.0261
613.0397
2.40E+08
28.63934
333.6808
2665196.3
27.61793
Std.
2.51E+02
8.65E+02
4.15E+07
1.55E−01
6.60E+02
2.39E+06
5.96E−01
F17
Mean
0.098006
0.773343
113.1723
0.000606
0.113039
3.053331
0.002122
Std.
4.52E−02
7.81E−01
1.65E+01
5.14E−04
4.86E−02
1.94E+00
2.49E+03
F18
Mean
2471.722
5467.711
77849.98
8727.620
3786.828
9466.5033
33888.18
Std.
8.14E+02
2.17E+03
2.33E+04
3.27E+03
1.16E+03
5.05E+03
8.87E+03
F19
Mean
48.6756
266.4841
963.9342
133.7175
57.1907
258.12907
489.6096
Std.
2.05E+01
4.34E+01
6.46E+01
6.75E+01
2.12E+01
1.02E+02
1.07E+02
F20
Mean
0.023375
12.942495
984.33553
1.86E−87
0.002295
32.495835
5.38E−99
Std.
2.56E−02
2.08E+01
3.88E+02
9.79E−87
2.08E−03
2.40E+01
3.74E−98
With increasing dimensions, the number of optima in the multimodal function exponentially increases. The optimization results of F21–F29 in Table 6 shows that DFA yields superior optimization results for the multimodal functions, even in the case of high dimensions. Even if the optimal result cannot be obtained, DFA yields a result close to the optimal.
Optimization results of high-dimensional multi-peaked functions
DFA
ABC
FA
GWO
HS
GOA
WOA
F21
Mean
0.000411
1.550200
167.22363
0.00
0.005283
200.2107
8.29E−16
Std.
2.82E−04
3.52E+00
2.12E+01
0.00
3.44E−03
6.42E+01
1.93E−15
F22
Mean
0.317133
175.69147
594.55982
0.000338
1.059683
62.931834
0.002985
Std.
1.06E−01
2.28E+01
7.17E+01
2.46E−03
2.43E−02
3.06E+01
6.64E−03
F23
Mean
0.360416
0.920308
62.836655
2.20E−54
0.015389
24.326460
15.101463
Std.
3.53E−01
5.43E−01
5.93E+00
1.03E−53
1.18E−02
6.25E+00
1.32E+01
F24
Mean
0.020931
0.064906
22.508559
0.039639
6.34E−05
4.132649
1.129130
Std.
5.80E−02
1.25E−01
3.83E+00
1.81E−02
7.11E−05
2.14E+00
1.85E+00
F25
Mean
0.011110
0.181606
4.894935
0.232352
0.001197
2.444549
0.369730
Std.
1.05E−02
2.99E−01
9.85E−01
1.32E−01
2.41E−03
1.25E+00
4.82E−01
F26
Mean
5.17E−16
1.76E−13
7.89E−08
−0.066666
6.01E−16
2.43E−11
5.35E−13
Std.
4.65E−16
9.66E−14
9.06E−08
2.92E−01
5.62E−16
3.10E−11
5.25E−13
F27
Mean
2.009873
13.113274
22.730518
0.093215
1.780434
9.256540
0.239882
Std.
3.14E−01
1.82E+00
3.04E+00
2.91E−02
2.99E−01
1.89E+00
1.39E−01
F28
Mean
−3.498860
−1.060149
−0.012483
−1.272104
−2.083275
−0.090718
−3.149778
Std.
7.94E−04
6.29E−01
6.58E−02
4.85E−01
1.24E+00
1.58E−01
5.92E−01
F29
Mean
−12077.65
−7918.84
−2253.11
−8623.52
−12544.84
−6294.25
−8926.81
Std.
4.45E+02
6.00E+02
5.28E+02
1.01E+03
3.47E+01
4.29E+02
1.01E+03
Fixed-dimensional functions usually comprise several low-dimensional functions, which greatly test the exploitation and exploration abilities of metaheuristic algorithms. Metaheuristic algorithms that balance the exploitation and exploration capabilities could obtain optimization results with better chance. Table 7 shows that DFA achieves the best overall optimization results and has balanced development and exploration capabilities.
Optimization results of fixed-dimensional functions
DFA
ABC
FA
GWO
HS
GOA
WOA
F30
Mean
0.000426
0.001370
0.001605
0.000839
0.010827
0.005892
0.000558
Std.
2.95E−04
5.74E−04
5.32E−04
2.06E−03
1.79E−02
8.89E−03
6.12E−04
F31
Mean
−3.862782
−3.861855
−3.862654
−3.862318
−3.862651
−3.862782
−3.862782
Std.
2.49E−15
1.00E−03
3.42E−04
2.24E−03
9.51E−04
2.29E−13
1.90E−15
F32
Mean
−8.312070
−7.740637
−6.395885
−9.572651
−5.361369
−5.553447
−6.807390
Std.
3.10E+00
2.30E+00
1.58E+00
1.89E+00
3.27E+00
3.38E+00
3.52E+00
F33
Mean
−45.77847
−45.05114
−19.19658
−40.97316
−45.77847
−42.02
−45.77846
Std.
2.20E−10
1.22E+00
5.66E+00
3.06E+00
2.45E−08
4.14E+00
5.82E−05
F34
Mean
0.001695
0.002405
0.037276
0.001066
0.003754
1.32E−05
0.001757
Std.
6.01E−03
2.76E−03
6.70E−02
3.26E−03
6.64E−03
2.60E−05
3.55E−03
F35
Mean
5.206178
23.54695
16.389712
10.087391
108.71767
0.780104
23.194003
Std.
2.00E+01
3.84E+01
2.19E+01
3.43E+01
1.76E+02
2.17E+02
1.21E+02
Overall, DFA shows best performance compared to other algorithms in the low-dimensional single-peaked, low-dimensional multi-peaked, and fixed-dimensional functions; and also achieves better results than most algorithms in the high-dimensional functions.
Statistical Analysis
Although the optimization results show that DFA exhibits better overall performance than other metaheuristic algorithms, the superior performance of DFA needs to be demonstrated in statistical analysis. This section uses the classic statistical analysis method, i.e., the rank-sum ratio (RSR) [46], to conduct statistical analysis. The RSR values embody information on all evaluation indicators, showing the comprehensive level of these indicators. The large RSR value indicates the merit of the algorithm. The optimization mean of the algorithm on the benchmark functions is used as an evaluation indicator. The analysis results of RSR are given in Table 8, which shows that DFA exhibits better overall performance than other metaheuristic algorithms.
Rank-sum ratio result
Algorithm
RSR ranking
Probit
RSR fitted values
Grading level
DFA
1
6.802743090739191
0.9912724183765839
6
GWO
2
6.067570523878141
0.8917274765228165
5
WOA
3
5.565948821932863
0.8238061402222359
5
ABC
4
5.1800123697927045
0.7715489921466842
4
HS
5
4.819987630207295
0.722800380754713
4
FA
7
3.9324294761218583
0.6026218963785807
3
GOA
6
4.434051178067137
0.6705432326791614
3
Convergence Analysis
Fig. 4 shows the convergence curves of DFA along with those of other algorithms on some of the benchmark functions. The figure shows that DFA usually converges quickly at the beginning and the convergence speed starts slowing down shortly after the beginning; thus, the overall convergence rate of DFA is not fast, which is attributed to DFA’s conservative exploitation strategy. However, the continuous update of DFA during subsequent iterations enables a preferable exploration capability that prevents DFA from falling into the local optimum. In most cases, DFA converges to the optimum at 1/3 of the iterations.
Convergence curves of DFA and comparison algorithms on some benchmark functions
DFA for Engineering Projects
In this section, DFA is applied to five constrained engineering design problems: welded beam design, pressure vessel design, three-bar truss design, compression spring design, and cantilever beam design. All the algorithms are tested for each engineering project in 30 independent runs with 500 iterations per run and the best value is taken as the final optimization result.
Welded Beam Design
Welded beam design is a common engineering optimization problem where the objective is to find the optimum length of the clamped bar l, height of the bar t, thickness of the bar b, and weld thickness h of a beam bar to minimize the manufacturing cost of a welded beam (Fig. 5). The cost of a welded beam is formulated as
minf(x→)=1.10471x12x2+0.04811x3x4(14+x2)x→=[x1x2x3x4]=[hltb]
Diagram of welded beam design
Subject to
g1(x→)=τ(x→)−τmax≤0,g2(x→)=σ(x→)−σmax≤0,g3(x→)=x1−x4≤0,g4(x→)=0.10471x12+0.04811x3x4(14+x2)−5≤0,g5(x→)=0.125−x1≤0,g6(x→)=δ(x→)−δmax≤0,g7(x→)=P−Pc(x→)≤0
Variable range
0.1≤x1≤2,0.1≤x2≤10,0.1≤x3≤10,0.1≤x4≤2
The constraints and their associated constants are expressed as follows:
τ(x→)=(τ′)2+2τ′τ″x22R+(τ″)2τ′=P2x1x2,τ″=MRJ,M=P(L+x2/2)R=x224+(x1+x3)24J=2[2x1x2(x2212+(x1+x3)24)]σ(x→)=6PLx4x32,Pc(x→)=4.013Ex32x4636L2(1−x32LE4G)P=6000lb,L=14in,E=30×106psiG=12×106psi,τmax=13,600psi,σmax=30,000psi,δmax=0.25in
The results of DFA are compared with those of the other algorithms, and the data in Table 9 show that DFA yields the lowest manufacturing cost, indicating that DFA has the potential to solve the welded beam design problem.
Comparison results for welded beam design
Algorithm
Optimum variables
Optimum cost
h
l
t
b
DFA
0.2053263
3.47659665
9.04334824
0.20569611
1.72595568
FA
0.26841149
3.15279017
7.65984043
0.29365304
2.10712421
ABC
0.19403412
3.66950515
9.22029126
0.2075474
1.77937345
GWO
0.19870343
3.64395848
9.00965781
0.20696315
1.74176404
HS
0.25534331
3.12713781
7.63445357
0.28824088
2.03847263
GOA
0.18912920
3.85154748
9.03662298
0.20572969
1.74982934
WOA
0.18719115
7.44065593
8.89750281
0.2122135
2.23569138
Pressure Vessel Design
DFA is employed for the pressure vessel design problem. This design aims to find the appropriate shell (Ts=x1), thickness of the head (Th=x2), inner radius (R=x3), and length of the shell (L=x4) to minimize the total material cost, incorporating four constraints: Ts and Th are integer multiples of 0.625 and R and L are continuous variables. Fig. 6 shows the dimensions of the pressure vessel structure. The cost of the pressure vessel design is formulated as
minf(x→)=0.6224x1x3x4+1.7781x2x32+3.1661x12x4+19.84x12x3
Diagram of pressure vessel design
Subject to
g1=−x1+0.0193x3≤0,g2=−x2+0.00954x3≤0,g3=−πx32x4−43πx33+1296000≤0,g4=x4−240≤0,
Variable range
0≤x1≤99,0≤x2≤99,10≤x3≤200,10≤x4≤200,
Table 10 presents the optimization results of DFA and other algorithms and shows that DFA yields the best optimization results.
Comparison results for pressure vessel design
Algorithm
Optimum variables
Optimum cost
Ts
Th
R
L
DFA
12.68830574
6.27191504
41.0895908
189.55214995
5911.15146639
ABC
12.70506601
6.27994045
41.13437792
188.9686432
5914.386945161
FA
15.06349385
7.4636159
48.13021892
114.0638514
7810.28453922
GWO
13.32095422
6.82361653
43.10752296
164.5239233
6038.11905684
HS
13.19592313
6.52452479
42.72899153
169.00003781
5971.21817933
GOA
12.88471723
6.36890596
41.72542605
181.31441634
5933.29373914
WOA
13.4869777
6.66662006
43.67544592
158.02818082
6005.51170785
Three-Bar Truss Design
The three-bar truss design is the third case study employed. It aims to evaluate the optimum cross-sectional areas A1=A3=x1 and A2=x2 so that the volume of the static load truss structure is minimized and constrained considering the stress (σ). Fig. 7 displays the dimensions of the three-bar truss design. The volume equation of the truss structure is
minf(x→)=(22x1+x2)×H,
Diagram of pressure vessel design
Subject to
g1=2x1+x22x12+2x1x2P−σ≤0,g2=x22x12+2x1x2P−σ≤0,g3=1x1+2x2P−σ≤0,
Variable range
0≤x1≤1,0≤x2≤1,H=100cm,P=2KN/cm2,σ=2KN/cm2
The statistical results of DFA and the other algorithms for the three-bar truss design problem are shown in Table 11, indicating that DFA yields the lowest volume compared to the other optimization algorithms.
Comparison results for three-bar truss design
Algorithm
Optimum variables
Optimum volume
A1=A3
A2
DFA
0.78875500
0.40802178
263.89578211
ABC
0.78873793
0.40811035
263.89980965
FA
0.78868058
0.40823232
263.89578701
GWO
0.78856879
0.40855575
263.89651005
HS
0.78764908
0.41115752
263.89655385
GOA
0.78881723
0.40784588
263.89579234
WOA
0.7883832
0.40907398
263.89584001
Compression Spring Design
Compression spring design aims to minimize the mass f(x) under certain constraints, including four inequality constraints of minimum deflection, shear stress, surge frequency, and deflection. Three design variables are present: the mean coil diameter (D), wire diameter (d), and number of active coils (N). Fig. 8 shows the dimensions of the compression spring design. The mass equation of the compression spring is
minf(x)=(N+2)Dd2
Diagram of compression spring design
Subject to
g1(x)=1−D3N71785d4≤0g2(x)=4D2−dD12566(Dd3−d4)+15108d2−1≤0g3(x)=1−140.45dD2N≤0g4(x)=D+d1.5−1≤0
Variable range
0.05≤d≤2,0.25≤D≤1.3,2≤N≤15
The statistical results of DFA and the other algorithms for the three-bar truss design problem are shown in Table 12. DFA yields the lowest mass, denoting that DFA can well solve the compression spring design problem.
Comparison results for compression spring design
Algorithm
Optimum variables
Optimum mass
d
D
N
DFA
0.05180635
0.35954649
11.12500622
0.012665467
ABC
0.05227057
0.35384607
12.16672679
0.013696147
FA
0.05
0.31667258
14.24697868
0.01286243
GWO
0.05298374
0.38866847
9.65846781
0.01272055
HS
0.05581944
0.46445987
6.95555321
0.0129601920
GOA
0.05362217
0.40503082
8.93195867
0.012731378
WOA
0.05126438
0.34658734
11.90853585
0.012668523
Cantilever Beam Design
Cantilever beam design is a structural engineering design problem related to the weight optimization of the square section cantilever. The cantilever beam is rigidly supported at one end, and a vertical force acts on the free node of the cantilever. The beam comprises five hollow square blocks of constant thickness, the height (or width) of which is the decision variable and the thickness is fixed. Fig. 9 depicts the dimensions of the cantilever beam design. The weight equation of the cantilever beam is
f(X)=0.0624(x1+x2+x3+x4+x5)
Diagram of cantilever beam design
Subject to
g(X)=61x13+37x23+19x33+7x43+1x53−1≤0
Variable range
0.01≤xi≤100,i=1,2⋯,5
The statistical results of DFA and the other algorithms for the cantilever beam design problem are shown in Table 13. The table shows that DFA outperforms all other algorithms except GOA. Although DFA achieves suboptimal optimization results, this result is acceptable according to NFL.
Comparison results for cantilever beam design
Algorithm
Optimum variables
Optimum weight
x1
x2
x3
x4
x5
DFA
5.96871891
5.35265306
4.53393307
3.475844
2.14508805
1.34011719
ABC
6.34433249
5.25405738
4.83394417
3.12836834
2.0932221
1.35120489
FA
5.98319073
5.38116037
4.44248883
3.44473814
2.26317699
1.34302424
GWO
6.18084273
5.11890478
4.66205867
3.36104363
2.19997452
1.34302424
HS
6.11954396
5.39091533
4.42759653
3.46660407
2.08273114
1.34081320
GOA
6.01598408
5.30917278
4.4945209
3.50137541
2.15260648
1.33995636
WOA
9.53170142
4.20385935
3.62127756
12.87413556
3.28738588
2.091545650
Conclusion
This study presented a novel metaheuristic algorithm called DFA. The effectiveness of DFA was validated on 35 well-known benchmark functions and compared with that of six well-known metaheuristic algorithms. The optimization capabilities of DFA were examined in terms of exploitation capability, statistical analyses, and convergence analyses. The results indicate that DFA is a competitive metaheuristic algorithm with outstanding performance in terms of global optimization problems.
DFA was also applied to five engineering design problems for verification in practical applications (i.e., welded beam, pressure vessel, three-bar truss, compression spring, and cantilever beam design problems). DFA outperforms the other six metaheuristic algorithms in the chosen evaluation criteria. In subsequent studies, DFA will be used to improve the efficiency of machine learning potential development for Fe–Cr–Al ternary alloys.
Funding Statement
This work is performed under collaboration with College of Materials Science and Chemical Engineering, Harbin Engineering University by the support of National Key Research and Development Program of China (2019YFB1901003). The authors also acknowledge the financial support of National Natural Science Foundation of China (Grants No. 52250005, 21875271, 21707147, 11604346, 21671195, and 51872302), the Key R & D Projects of Zhejiang Province No. 2022C01236, the Zhejiang Province Key Research and Development Program (No. 2019C01060), and the project of the key technology for virtue reactors from NPIC. Entrepreneurship Program of Foshan National Hi-tech Industrial Development Zone.
Conflicts of Interest
The authors declare that they have no conflicts of interest to report regarding the present study.
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