There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Population-based optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposed AMBO algorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the population can play a role in updating the population matrix. The theory of AMBO is described and then mathematically modeled for implementation on optimization problems. The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions, which belong to three different categories: unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO, eight other well-known algorithms have been also implemented. The optimization results demonstrate the ability of AMBO to solve various optimization problems. Also, comparison and analysis of the results show that AMBO is superior and more competitive than the other mentioned algorithms in providing suitable solution.

Optimization is defined as finding the best solution out of all possible solutions to a problem by considering the constraints and limitations. Therefore, each optimization problem consists of three main parts: decision variables, primary objectives, and secondary objectives. The decision variables are the same as the problem variables, the primary objectives represent the constraints of the problem, and the secondary objectives are the objective functions of the problem.

Population-based optimization algorithms (PBOAs) are one of the most effective methods for solving optimization problems. PBOAs are able to provide appropriate solutions to optimization problems based on random scan of the search space and through an iterative-based process [

PBOAs are designed based on simulation of various natural phenomena, behavior of living organisms, physical laws, genetic sciences, rules of the games, and so on. In a general classification based on the design idea, PBOAs are categorized into 4 groups: swarm-based, physics-based, evolutionary-based, and game-based optimization algorithms.

Swarm-based optimization algorithms are designed based on simulating the behavior of living organisms such as animals, plants, and natural phenomena. Particle Swarm Optimization (PSO) is one of the most widely-used algorithms in this category, which is based on simulating behaviors of birds’ swarm [

Physics-based optimization algorithms are introduced based on simulation of various laws of physics. Spring Search Algorithm (SSA) is one of the algorithms in this group, which was designed based on the simulation of Hooke’s law in a system consisting of weights and springs [

Evolutionary-based optimization algorithms are inspired by genetics and inheritance laws. Genetic Algorithm (GA) is the most famous algorithm in this group, which is designed based on simulation of reproduction process and Darwin’s theory of evolution by natural selection [

Game-based optimization algorithms are another POBAs, which are designed based on simulating rules of various games. Football Game-Based Optimization (FGBO) is one of the algorithms in this group, which was introduced based on simulation of football league rules and clubs’ behaviors [

In this paper, a new optimization algorithm entitled All Members-Based Optimizer (AMBO) is designed to provide suitable quasi-optimal solutions for various optimization problems. In the proposed AMBO, all members of the population, regardless of their position in the search space, participate in updating the population matrix. Various steps of implementing AMBO are explained and then its mathematical formulation is presented. The performance of AMBO in providing quasi-optimal solution is evaluated for twenty-three standard objective functions of different types.

The rest of the article is organized as follows. In Section 2, the proposed AMBO algorithm is described and modeled. In Section 3, the proposed algorithm is simulated for optimizing different objective functions and the results are presented. Statistical analysis of the results is carried out in Section 4. Finally, the conclusions of this investigation and suggestions for future studies are presented in Section 5.

In this section, various steps and mathematical modeling of the proposed optimization algorithm are presented. All Members-Based Optimizer (AMBO) is a PBOA proposed for solving optimization problems. The main idea in designing AMBO is to make more use of the population matrix information as well as the simultaneous participation of all members of the population in updating the algorithm population. The search space for each optimization problem consists of coordinate axes equal to the number of problem variables. In most of optimization algorithms, the best population member directs the population of the algorithm along these axes. Also, in some algorithms, the worst member or several members with specific characteristics are effective in updating the algorithm population. However, an ordinary member of the population may be more qualified to lead the population in some axes than the best member. Therefore, AMBO is designed based on this concept to use the information of all population members.

Each PBOA has a number of members called the algorithm population. The algorithm population can be displayed using a matrix called the population matrix. Each row of this matrix represents a population member and each column of this matrix represents a variable of the optimization problem. Therefore, the number of rows in the population matrix is equal to the number of population members and the number of columns in this matrix is equal to the number of the optimization problem variables.

In AMBO, the population matrix is represented using

In each iteration of the algorithm, the objective function of the problem is evaluated based on the suggested values of variables provided by each population member. Therefore, the values of the objective function are specified as a vector using

In the proposed AMBO algorithm, population members are updated in two stages. In the first stage, each member of the population is updated based on the position of different members of the population in the search space. The important point in this process is that the new position is acceptable to a population member if it improves the value of the objective function. Otherwise, the update is not acceptable and the member remains in its previous position. The first stage is simulated using

In the second stage, the population matrix is updated based on the best member. In this stage, similar to the first stage, the new position is acceptable to a population member if it improves the objective function. The second stage of AMBO is simulated in

For each iteration, the population matrix is updated through these two steps and this process is repeated until the algorithm stops. At the end of the algorithm iterations, AMBO provides the best obtained quasi-optimal solution to the optimization problem. The implementation process of the proposed algorithm in an optimization problem is shown as a flowchart in

In this section, the ability of AMBO for solving optimization problems and providing quasi-optimal solutions is evaluated. For this purpose, AMBO is implemented on a set of twenty-three standard objective functions from three different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. Complete information of these objective functions is provided in the Appendix (

In order to analyze the performance of the proposed AMBO in providing the quasi-optimal solution, AMBO is compared with eight other optimization algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), and Tunicate Swarm Algorithm (TSA).

To solve each of the objective functions, 20 independent runs of the proposed AMBO has been performed, where each run includes 1000 iterations. The average (Ave) and standard deviation (std) of the best solutions have been used to present the results of optimization for the objective functions.

The objective functions _{1} to _{7} belong to unimodal category. These functions have been selected to evaluate the performance of the optimization algorithms. The optimization results for these objective functions using the proposed AMBO and eight other optimization algorithms are presented in _{1}, _{2}, _{3}, _{4}, and _{6} objective functions.

AMBO | MPA | TSA | GOA | GWO | TLBO | GSA | PSO | GA | ||
---|---|---|---|---|---|---|---|---|---|---|

F_{1} |
Ave | 0 | 3.2715E-21 | 7.71E-38 | 2.1741E-09 | 1.09E-58 | 8.3373E-60 | 2.0255E-17 | 1.7740E-05 | 13.2405 |

Std | 0 | 4.6153E-21 | 7.00E-21 | 7.3985E-25 | 5.1413E-74 | 4.9436E-76 | 1.1369E-32 | 6.4396E-21 | 4.7664E-15 | |

F_{2} |
Ave | 0 | 1.57E-12 | 8.48E-39 | 0.5462 | 1.2952E-34 | 7.1704E-35 | 2.3702E-08 | 0.3411 | 2.4794 |

Std | 0 | 1.42E-12 | 5.92E-41 | 1.7377E-16 | 1.9127E-50 | 6.6936E-50 | 5.1789E-24 | 7.4476E-17 | 2.2342E-15 | |

F_{3} |
Ave | 0 | 0.0864 | 1.15E-21 | 1.7634E-08 | 7.4091E-15 | 2.7531E-15 | 279.3439 | 589.4920 | 1536.8963 |

Std | 0 | 0.1444 | 6.70E-21 | 1.0357E-23 | 5.6446E-30 | 2.6459E-31 | 1.2075E-13 | 7.1179E-13 | 6.6095E-13 | |

F_{4} |
Ave | 0 | 2.6E-08 | 1.33E-23 | 2.9009E-05 | 1.2599E-14 | 9.4199E-15 | 3.2547E-09 | 3.9634 | 2.0942 |

Std | 0 | 9.25E-09 | 1.15E-22 | 1.2121E-20 | 1.0583E-29 | 2.1167E-30 | 2.0346E-24 | 1.9860E-16 | 2.2342E-15 | |

F_{5} |
Ave | 20.1578 | 46.049 | 28.8615 | 41.7767 | 29.8607 | 146.4564 | 36.10695 | 50.26245 | 310.4273 |

Std | 2.22E-14 | 0.4219 | 4.76E-03 | 2.5421E-14 | 6.95E-13 | 1.9065E-14 | 3.0982E-14 | 1.5888E-14 | 2.0972E-13 | |

F_{6} |
Ave | 0 | 0.398 | 7.10E-21 | 1.6085E-09 | 0.6423 | 0.4435 | 0 | 20.25 | 14.55 |

Std | 0 | 0.1914 | 1.12E-25 | 4.6240E-25 | 6.2063E-17 | 4.2203E-16 | 0 | 0 | 3.1776E-15 | |

F_{7} |
Ave | 1.41E-05 | 0.0018 | 3.72E-04 | 0.0205 | 0.0008 | 0.0017 | 0.0206 | 0.1134 | 5.6799E-03 |

Std | 6.06E-21 | 0.0010 | 5.09E-05 | 1.5515E-18 | 7.2730E-20 | 3.87896E-19 | 2.7152E-18 | 4.3444E-17 | 7.7579E-19 |

Six high-dimensional multimodal objective functions _{8} to _{13} have been selected to evaluate the performance of the optimization algorithms in providing a suitable quasi-optimal solution. _{8}, _{9}, and _{10} objective functions.

AMBO | MPA | TSA | GOA | GWO | TLBO | GSA | PSO | GA | ||
---|---|---|---|---|---|---|---|---|---|---|

F_{8} |
Ave | −9164.21 | −3594.16321 | −5740.3388 | −1663.9782 | −5885.1172 | −7408.6107 | −2849.0724 | −6908.6558 | −8184.4142 |

Std | 2.03E-13 | 811.32651 | 41.5 | 716.3492 | 467.5138 | 513.5784 | 264.3516 | 625.6248 | 833.2165 | |

F_{9} |
Ave | 0 | 140.1238 | 5.70E-03 | 4.2011 | 8.5265E-15 | 10.2485 | 16.2675 | 57.0613 | 62.4114 |

Std | 0 | 26.3124 | 1.46E-03 | 4.3692E-15 | 5.6446E-30 | 5.5608E-15 | 3.1776E-15 | 6.3552E-15 | 2.5421E-14 | |

F_{10} |
Ave | 4.44E-15 | 9.6987E-12 | 9.80E-14 | 0.3293 | 1.7053E-14 | 0.2757 | 3.5673E-09 | 2.1546 | 3.2218 |

Std | 2.65E-35 | 6.1325E-12 | 4.51E-12 | 1.9860E-16 | 2.7517E-29 | 2.5641E-15 | 3.6992E-25 | 7.9441E-16 | 5.1636E-15 | |

F_{11} |
Ave | 0 | 0 | 1.00E-07 | 0.1189 | 0.0037 | 0.6082 | 3.7375 | 0.0462 | 1.2302 |

Std | 0 | 0 | 7.46E-07 | 8.9991E-17 | 1.2606E-18 | 1.9860E-16 | 2.7804E-15 | 3.1031E-18 | 8.4406E-16 | |

F_{12} |
Ave | 5.13E-07 | 0.0851 | 0.0368 | 1.7414 | 0.0372 | 0.0203 | 0.0362 | 0.4806 | 0.0470 |

Std | 4.74E-22 | 0.0052 | 1.5461E-02 | 8.1347E-12 | 4.3444E-17 | 7.7579E-19 | 6.2063E-18 | 1.8619E-16 | 4.6547E-18 | |

F_{13} |
Ave | 9.68E-06 | 0.4901 | 2.9575 | 0.3456 | 0.5763 | 0.3293 | 0.0020 | 0.5084 | 1.2085 |

Std | 4.65E-18 | 0.1932 | 1.5682E-12 | 3.25391E-12 | 2.4825E-16 | 2.1101E-16 | 4.2617E-14 | 4.9650E-17 | 3.2272E-16 |

The third group of objective functions, including _{14} to _{23}, is selected from the fixed-dimensional multimodal type. The results of the implementation of the proposed algorithm and eight other optimization algorithms on these objective functions are presented in _{15}, _{16}, and _{17} target functions.

AMBO | MPA | TSA | GOA | GWO | TLBO | GSA | PSO | GA | ||
---|---|---|---|---|---|---|---|---|---|---|

F_{14} |
Ave | 0.998004 | 0.9980 | 1.9923 | 0.9980 | 3.7408 | 2.2721 | 3.5913 | 2.1735 | 0.9986 |

Std | 2.23E-16 | 4.2735E-16 | 2.6548E-07 | 9.4336E-16 | 6.4545E-15 | 1.9860E-16 | 7.9441E-16 | 7.9441E-16 | 1.5640E-15 | |

F_{15} |
Ave | 0.000307 | 0.0030 | 0.0004 | 0.0049 | 0.0063 | 0.0033 | 0.0024 | 0.0535 | 5.3952E-02 |

Std | 5.82E-18 | 4.0951E-15 | 9.0125E-04 | 3.4910E-18 | 1.1636E-18 | 1.2218E-17 | 2.9092E-19 | 3.8789E-19 | 7.0791E-18 | |

F_{16} |
Ave | −1.03163 | −1.0316 | −1.0316 | −1.0316 | −1.0316 | −1.0316 | −1.0316 | −1.0316 | −1.0316 |

Std | 7.45E-16 | 4.4652E-16 | 2.6514E-16 | 9.9301E-16 | 3.9720E-16 | 1.4398E-15 | 5.9580E-16 | 3.4755E-16 | 7.9441E-16 | |

F_{17} |
Ave | 0.397887 | 0.3979 | 0.3991 | 0.4047 | 0.3978 | 0.3978 | 0.3978 | 0.7854 | 0.4369 |

Std | 4.97E-17 | 9.1235E-15 | 2.1596E-16 | 2.4825E-17 | 8.6888E-17 | 7.4476E-17 | 9.9301E-17 | 4.9650E-17 | 4.9650E-17 | |

F_{18} |
Ave | 3 | 3 | 3 | 3 | 3.0000 | 3.0009 | 3 | 3 | 4.3592 |

Std | 1.19E-15 | 1.9584E-15 | 2.6528E-15 | 5.6984E-15 | 2.0853E-15 | 1.5888E-15 | 6.9511E-16 | 3.6741E-15 | 5.9580E-16 | |

F_{19} |
Ave | −3.86278 | −3.8627 | −3.8066 | −3.8627 | −3.8621 | −3.8609 | −3.8627 | −3.8627 | −3.85434 |

Std | 2.78E-15 | 4.2428E-15 | 2.6357E-15 | 3.1916E-15 | 2.4825E-15 | 7.3483E-15 | 8.3413E-15 | 8.9371E-15 | 9.9301E-17 | |

F_{20} |
Ave | −3.322 | −3.3211 | −3.3206 | −3.2424 | −3.2523 | −3.2014 | −3.0396 | −3.2619 | −2.8239 |

Std | 3.28E-15 | 1.1421E-11 | 5.6918E-15 | 7.9441E-16 | 2.1846E-15 | 1.7874E-15 | 2.1846E-14 | 2.9790E-16 | 3.97205E-16 | |

F_{21} |
Ave | −10.1532 | −10.1532 | −5.5021 | −7.4016 | −9.6452 | −9.1746 | −5.1486 | −5.3891 | −4.3040 |

Std | 6.75E-15 | 2.5361E-11 | 5.4615E-13 | 2.3819E-11 | 6.5538E-15 | 8.5399E-15 | 2.9790E-16 | 1.4895E-15 | 1.5888E-15 | |

F_{22} |
Ave | −10.4029 | −10.4029 | −5.0625 | −8.8165 | −10.4025 | −10.0389 | −9.0239 | −7.6323 | −5.1174 |

Std | 2.78E-15 | 2.8154E-11 | 8.4637E-14 | 6.7524E-15 | 1.9860E-15 | 1.5292E-14 | 1.6484E-12 | 1.5888E-15 | 1.2909E-15 | |

F_{23} |
Ave | −10.5364 | −10.5364 | −10.3613 | −10.0003 | −10.1302 | −9.2905 | −8.9045 | −6.1648 | −6.5621 |

Std | 6.75E-15 | 3.9861E-11 | 7.6492E-12 | 9.1357E-15 | 4.5678E-15 | 1.1916E-15 | 7.1497E-14 | 2.7804E-15 | 3.8727E-15 |

Two important indicators for evaluating the performance of the optimization algorithms in solving optimization problems are the exploitation index and the exploration index.

Exploitation index indicates the ability of an optimization algorithm to provide a suitable quasi-optimal solution close to the global optimum for an optimization problem. An optimization algorithm must be able to provide a suitable solution at the end of its iterations. whatever this solution is closer to the global optimum, that algorithm has higher exploitation power. Unimodal objective functions (_{1} to _{7}) have only one optimal solution. Therefore, they are suitable for evaluating the exploitation power of the optimization algorithms. The optimization results of these objective functions show that AMBO has the best performance for all _{1} to _{7} functions and has higher exploitation power than the other eight optimization algorithms.

Exploration power means the ability of an algorithm to scan the search space properly and accurately. This indicator is especially important for optimization problems that have several local optimal solutions. Therefore, an algorithm that can provide a suitable quasi-optimal solution by accurately scanning the search space and passing through local optimal solutions has high exploration power. The multimodal objective functions of the second and third groups (_{8} to _{13} and _{14} to _{23}) have several local optimums. Hence, they are suitable for evaluating exploration power. The optimization results of these objective functions presented in

Objective function | Maximum number of iterations | |||
---|---|---|---|---|

100 | 500 | 800 | 1000 | |

F_{1} |
8.4E-178 | 0 | 0 | 0 |

F_{2} |
2.41E-90 | 0 | 0 | 0 |

F_{3} |
1.02E-53 | 5.1E-299 | 0 | 0 |

F_{4} |
6.17E-76 | 0 | 0 | 0 |

F_{5} |
23.9188 | 22.79437 | 21.83367 | 21.25926 |

F_{6} |
0 | 0 | 0 | 0 |

F_{7} |
0.000166 | 3.87E-05 | 2.52E-05 | 2.31E-05 |

F_{8} |
−6388.5 | −8931.93 | −9124.55 | −9416.32 |

F_{9} |
0 | 0 | 0 | 0 |

F_{10} |
4.44E-15 | 4.44E-15 | 4.44E-15 | 4.44E-15 |

F_{11} |
0 | 0 | 0 | 0 |

F_{12} |
0.000106 | 5.11E-06 | 2.18E-06 | 7.46E-07 |

F_{13} |
0.100696 | 0.110341 | 2.79E-05 | 9.21E-06 |

F_{14} |
0.998004 | 0.998004 | 0.998004 | 0.998004 |

F_{15} |
0.020363 | 0.000307 | 0.000307 | 0.000307 |

F_{16} |
−1.03163 | −1.03163 | −1.03163 | −1.03163 |

F_{17} |
0.397888 | 0.397887 | 0.397887 | 0.397887 |

F_{18} |
3 | 3 | 3 | 3 |

F_{19} |
−3.86278 | −3.86278 | −3.86278 | −3.86278 |

F_{20} |
−3.31399 | −3.32199 | −3.322 | −3.322 |

F_{21} |
−10.1519 | −10.1532 | −10.1532 | −10.1532 |

F_{22} |
−10.4023 | −10.4029 | −10.4029 | −10.4029 |

F_{23} |
−10.5356 | −10.5364 | −10.5364 | −10.5364 |

In this section, the sensitivity of the AMBO to the two parameters of maximum number of iteration and number of population members is evaluated.

In order to evaluate the sensitivity of the proposed algorithm to the maximum number of iterations, the AMBO for the maximum number of iterations of 100, 500, 800 and 1000 has been implemented independently on all objective functions. The results of these implementations are presented in

Also, in order to analyze the sensitivity of the proposed algorithm to the number of population members, the AMBO has been implemented independently for different populations with number of 20, 30, 50 and 80 members. The results of this simulation for the number of different members of the population are presented in

Objective function | Number of population members | |||
---|---|---|---|---|

20 | 30 | 50 | 80 | |

F_{1} |
0 | 0 | 0 | 0 |

F_{2} |
2.2E-277 | 0 | 0 | 0 |

F_{3} |
1.4E-248 | 0 | 0 | 0 |

F_{4} |
1.4E-235 | 0 | 0 | 0 |

F_{5} |
28.73851 | 22.06195 | 20.99871 | 19.90636 |

F_{6} |
0 | 0 | 0 | 0 |

F_{7} |
0.000123 | 2.84E-05 | 1.76E-05 | 1.73E-05 |

F_{8} |
−5685 | −8221.25 | −8342.12 | −8785.23 |

F_{9} |
0 | 0 | 0 | 0 |

F_{10} |
4.44E-15 | 4.44E-15 | 4.44E-15 | 4.44E-15 |

F_{11} |
0 | 0 | 0 | 0 |

F_{12} |
0.091831 | 2.51E-06 | 1.25E-06 | 2.1E-07 |

F_{13} |
1.417566 | 0.143312 | 0.110816 | 0.097375 |

F_{14} |
0.998004 | 0.998004 | 0.998004 | 0.998004 |

F_{15} |
0.000308 | 0.000307 | 0.000307 | 0.000307 |

F_{16} |
−1.03163 | −1.03163 | −1.03163 | −1.03163 |

F_{17} |
0.397888 | 0.397887 | 0.397887 | 0.397887 |

F_{18} |
3 | 3 | 3 | 3 |

F_{19} |
−3.86278 | −3.86278 | −3.86278 | −3.86278 |

F_{20} |
−3.32199 | −3.32199 | −3.322 | −3.322 |

F_{21} |
−10.1513 | −10.1531 | −10.1532 | −10.1532 |

F_{22} |
−10.4028 | −10.4029 | −10.4029 | −10.4029 |

F_{23} |
−10.5364 | −10.5364 | −10.5364 | −10.5364 |

In this section, statistical analysis on the optimization results obtained by different optimization algorithms is presented. Although presenting the results in the form of average and standard deviation provides useful information about the performance of optimization algorithms, statistical analysis of the results is also important for better evaluation. For this purpose, the Wilcoxon rank sum test has been used as a non-parametric statistical test to specify the significance of the results. Wilcoxon rank test is applied to specify whether the results obtained by the proposed AMBO are different from other eight optimization algorithms in a statistically significant way.

A

Compared algorithms | Unimodal | High-multimodal | Fixed-multimodal |
---|---|---|---|

AMBO |
0.015625 | 0.03125 | 0.001953 |

AMBO |
0.015625 | 0.03125 | 0.003906 |

AMBO |
0.03125 | 0.03125 | 0.019531 |

AMBO |
0.015625 | 0.03125 | 0.005859 |

AMBO |
0.015625 | 0.03125 | 0.011719 |

AMBO |
0.015625 | 0.03125 | 0.007813 |

AMBO |
0.015625 | 0.03125 | 0.003906 |

AMBO |
0.015625 | 0.0625 | 0.0625 |

Optimization algorithms are one of the most effective and widely-used methods in solving optimization problems in various fields of science and engineering. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) was presented for solving optimization problems. The proposed AMBO was designed to use more information of different members of the population and to participate all members in updating the algorithm population. AMBO was mathematically modeled and implemented on a set of twenty-three standard objective functions. Also, in order to analyze the results, AMBO was compared with eight optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), and Tunicate Swarm Algorithm (TSA).

The results of optimizing the unimodal objective functions showed that AMBO is more capable than other algorithms in solving such problems and therefore, it is superior considering the exploitation index. Also, the results of optimization for multimodal objective functions showed that AMBO with high exploration power is able to provide suitable quasi-optimal solutions for this type of functions. Based on the simulation results, it can be concluded that the proposed algorithm has an acceptable ability to solve various optimization problems and is superior and much more competitive than other mentioned optimization algorithms.

The authors suggest some ideas and perspectives for future studies. Design of the binary version as well as the multi-objective version of the AMBO is an interesting potential for future investigations. Apart from this, implementing AMBO on various optimization problems and real-world optimization problems can be considered as some significant contributions, as well.

Information on the twenty-three objective functions used in the simulations is presented in