An enhancing many-objective evolutionary algorithm using chaotic mapping and solution ranking mechanism for large-scale optimization

다국어 초록 (Multilingual Abstract)

There are many complex optimization problems in the real world, and various evolutionary algorithms are proposed to solve them. Recently, the many-objective evolutionary algorithm using a one-by-one selection strategy (1by1EA) adopts a convergence indicator and a distribution indicator to balance convergence and diversity. However, the algorithm is too random in initialization and the fitness evaluation of solutions in the mating selection is single, which leads to poor performance in solving large-scale problems. Therefore, this paper proposes an improved method called 1by1EA-CHV by using circle chaotic mapping and a solution ranking mechanism based on the hypervolume (HV) indicator. We first map each component of solutions into a certain value space to initialize the population. Then, we calculate the contribution of each partition divided based on HV and apply the aggregation method to guide the reallocation of fitness, which achieves the ranking of solutions by using it before the old calculation method. To validate the performance, experiments compared 1by1EA-CHV with 1by1EA and other seven many-objective algorithms on large-scale functions, and the differences between these algorithms were analyzed statistically by a non-parametric test. The results showed the superiority of 1by1EA-CHV in solving large-scale many-objective optimization problems with up to 2000 decision variables.

참고문헌 (Reference)

1 Cai, X., "Weight convergence analysis of DV-hop localization algorithm with gA" 24 : 18249-18258, 2020

2 Schutze, O., "Using the averaged Hausdorffdistance as a performance measure in evolutionary multi-objective optimization" 16 : 504-522, 2012

3 Sun, G., "Time and energy minimization communications based on collaborative beamforming for UAV networks : A multi-objective optimization method" 39 : 3555-3572, 2021

4 Cheng, R., "Test problems for large-scale multi-objective and many-objective optimization" 47 : 4108-4121, 2016

5 Coello, C. A. C., "Solving multi-objective optimization problems using an artificial immune system" 6 : 163-190, 2005

6 Wang, G., "Solving multi-objective fuzzy job-shop scheduling problem by a hybrid adaptive differential evolution algorithm" 2022

7 Gao, D., "Solving fuzzy job-shop scheduling problem using DE algorithm improved by a selection mechanism" 28 : 3265-3275, 2020

8 Bi, J., "Self-adaptive bat algorithm with genetic operations" 9 : 1284-1294, 2022

9 Roopa, C., "Segmenting ECG and MRI data using ant colony optimisation" 7 : 46-58, 2019

10 Sun, G., "Secure and energy-efficient UAV relay communications exploiting collaborative beamforming" 2022

11 Lakshminarayanan, S., "Scheduling energy storage unit with GWO for smart home integrated with renewable energy" 7 : 146-163, 2020

12 Zitzler, E., "SPEA2 : Improving the strength Pareto evolutionary algorithm" TIK 103-, 2001

13 Wang, Y., "SCCwalk : An efficient local search algorithm and its improvements for maximumweight clique problem" 280 : 103230-, 2020

14 Li, Z., "Partition-based five-axis tool path generation for freeform surfacemachining using a non-spherical tool" 58 : 248-262, 2021

15 Wang, J., "Parameter optimization of interval type-2 fuzzy neural networks based on PSO and BBBC methods" 6 : 247-257, 2019

16 Cui, Z., "Optimal leach protocol with modified bat algorithm for big data sensing systems in internet of things" 132 : 217-229, 2019

17 Zhao Dong ; Liu Lei ; Yu Fanhua ; Heidari Ali Asghar ; Wang Maofa ; Chen Huiling ; 무함마드칸, "Opposition-based ant colony optimization with all-dimension neighborhood search for engineering design" 한국CDE학회 9 (9): 1007-1044, 2022

18 Cui, Z., "Novel PIO algorithm with multiple selection strategies for many-objective optimization problems" 1 : 291-307, 2021

19 Gong, W., "Nonlinear equations solving with intelligent optimization algorithms : A survey" 1 : 15-32, 2021

20 Lu, H., "Multi-robot indoor environment map building based on multi-stage optimization method" 1 : 145-161, 2021

21 Li, H., "Multi-objective optimization problemswith complicated Pareto sets, MOEA/D and NSGA-II" 13 : 284-302, 2008

22 Zitzler, E., "Multi-objective evolutionary algorithms : A comparative case study and the strength Pareto approach" 3 : 257-271, 1999

23 Zhang, W., "Multi-direction update-based multi-objective particle swarm optimization for mixed no-idle flow-shop scheduling problem" 1 : 176-197, 2021

24 Wang, G., "Monarch butterfly optimization" 31 : 1995-2014, 2019

25 Zhang, Q., "MOEA/D with NBI-style Tchebycheffapproach for portfolio management" 1-8, 2010

26 Zhang, Q., "MOEA/D : A multi-objective evolutionary algorithm based on decomposition" 11 : 712-731, 2007

27 Zitzler, E., "Indicator-based selection in multiobjective search" 832-842, 2004

28 Wang, Y, "Improving evolutionary algorithms with information feedback model for large-scale manyobjective optimization" 2022

29 Hernández Gómez, R., "Improved metaheuristic based on the R2 indicator for many-objective optimization" 679-686, 2015

30 Bader, J., "HypE : An algorithm for fast hypervolume-based many-objective optimization" 19 : 45-76, 2011

31 Kundu Rohit ; Mallipeddi Rammohan, "HFMOEA: a hybrid framework for multi-objective feature selection" 한국CDE학회 9 (9): 949-965, 2022

32 Köppen, M., "Fuzzy-Paretodominance and its application in evolutionary multi-objective optimization" 399-412, 2005

33 Tian, Y., "Evolutionary large-scale multi-objective optimization : A survey" 54 : 1-34, 2021

34 Deb, K., "Evaluating the ε-domination basedmulti-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions" 13 : 501-525, 2005

35 Sun, G., "Energy efficient collaborative beamforming for reducing sidelobe in wireless sensor networks" 20 : 965-982, 2021

36 Hussain Shahid ; Jamwal Prashant K ; Van Vliet Paulette, "Design synthesis and optimization of a 4-SPS intrinsically compliant parallel wrist rehabilitation robotic orthosis" 한국CDE학회 8 (8): 1562-1575, 2021

37 Yang, Y., "Cluster-based niching differential evolution algorithm for optimizing the stable structures ofmetallic clusters" 149 : 416-423, 2018

38 Yu, Y., "CBSO : A memetic brain storm optimization with chaotic local search" 10 : 353-367, 2018

39 Sun, Z., "Applications of game theory in vehicular networks : A survey" 23 : 2660-2710, 2021

40 Liu, Z., "AnD : A many-objective evolutionary algorithm with angle-based selection and shift-based density estimation" 509 : 400-419, 2020

41 Zheng, W., "An improvedMOEA/D design for many-objective optimization problems" 48 : 3839-3861, 2018

42 Toktas, A., "An image encryption scheme based on an optimal chaotic map derived by multi-objective optimization using ABC algorithm" 105 : 1885-1909, 2021

43 Deb, K, "An evolutionary many-objective optimization algorithm using reference-point-based non-dominated sorting approach, part I: Solving problems with box constraints" 18 : 577-601, 2013

44 Tian, Y., "An evolutionary algorithm for large-scale sparse multi-objective optimization problems" 24 : 380-393, 2019

45 Wang, F, "An estimation of distribution algorithm for mixed-variable newsvendor problems" 24 : 479-493, 2020

46 He, C., "Adaptive offspring generation for evolutionary large-scale multi-objective optimization" 52 : 786-798, 2020

47 Hua, Y., "A survey of evolutionary algorithms for multi-objective optimization problems with irregular Pareto fronts" 8 : 303-318, 2021

48 Thakare, A. N., "A self-organised routing algorithm for cognitive radio-based wireless sensor networks using biologically-inspired method" 6 : 148-169, 2017

49 Wang, L., "A review of reinforcement learning based intelligent optimization for manufacturing scheduling" 1 : 257-270, 2021

50 Wang, F, "A particle swarm optimization algorithm for mixed-variable optimization problems" 60 : 100808-, 2021

51 Md. Rakibul Islam ; Syed Mithun Ali ; Amir Mohammad Fathollahi-Fard ; Golam Kabir, "A novel particle swarm optimization-based grey model for the prediction of warehouse performance" 한국CDE학회 8 (8): 705-727, 2021

52 Shadkam, E., "A novel improved cuckoo optimisation algorithm for engineering optimisation" 7 : 164-177, 2020

53 Horn, J., "A niched Pareto genetic algorithm for multi-objective optimization" 82-87, 1994

54 Elarbi, M., "A new decomposition-based NSGA-II for many-objective optimization" 48 : 1191-1210, 2018

55 Cai, X., "A multi-cloudmodel-based many-objective intelligent algorithm for efficient task scheduling in internet of things" 8 : 9645-9653, 2020

56 Liu, Y., "A many-objective evolutionary algorithm using a one-by-one selection strategy" 47 : 2689-2702, 2017

57 Zhang, X., "A knee point-driven evolutionary algorithm for many-objective optimization" 19 : 761-776, 2014

58 Cui, Z., "A hybrid many-objective optimization algorithm for coal green production problem" 33 : e6040-, 2021

59 Yang, S., "A grid-based evolutionary algorithm for many-objective optimization" 17 : 721-736, 2013

60 Yang, X., "A fuzzy decision variables framework for large-scalemulti-objective optimization" 2021

61 Deb, K., "A fast and elitist multi-objective genetic algorithm : NSGA-II" 6 : 182-197, 2002

62 Wang, X., "A clusterbased competitive particle swarm optimizer with a sparse truncation operator for multi-objective optimization" 71 : 101083-, 2022

63 Zhaomin Hu ; Yang Lan ; Zhixia Zhang ; Xingjuan Cai, "A Many-objective Particle Swarm Optimization Algorithm Based on Multiple Criteria for Hybrid Recommendation System" 한국인터넷정보학회 15 (15): 442-460, 2021