A dynamic multiple populations particle swarm optimization algorithm based on decomposition and prediction

Liu, Ruochen; Li, Jianxia; Fan, Jing; Jiao, Licheng

doi:10.1016/j.asoc.2018.08.015

Cited by 34 publications

(7 citation statements)

References 28 publications

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“…In the environment of DMaOPs, many excellent algorithms have been proposed. Liu et al proposed a dynamic multi-population particle swarm optimization algorithm (DP-DMPPSO) based on decomposition and prediction [82]. Using the archive update mechanism based on the objective space decomposition and the population prediction mechanism to accelerate the convergence, the results show that the algorithm has a good effect in DMaOPs processing.…”

Section: The Background Of Maopsmentioning

confidence: 99%

Two-Population Coevolutionary Algorithm with Dynamic Learning Strategy for Many-Objective Optimization

Gui

Wang

Wang³

2021

Mathematics

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Due to the complexity of many-objective optimization problems, the existing many-objective optimization algorithms cannot solve all the problems well, especially those with complex Pareto front. In order to solve the shortcomings of existing algorithms, this paper proposes a coevolutionary algorithm based on dynamic learning strategy. Evolution is realized mainly through the use of Pareto criterion and non-Pareto criterion, respectively, for two populations, and information exchange between two populations is used to better explore the whole objective space. The dynamic learning strategy acts on the non-Pareto evolutionary to improve the convergence and diversity. Besides, a dynamic convergence factor is proposed, which can be changed according to the evolutionary state of the two populations. Through these effective heuristic strategies, the proposed algorithm can maintain the convergence and diversity of the final solution set. The proposed algorithm is compared with five state-of-the-art algorithms and two weight-sum based algorithms on a many-objective test suite, and the results are measured by inverted generational distance and hypervolume performance indicators. The experimental results show that, compared with the other five state-of-the-art algorithms, the proposed algorithm achieved the optimal performance in 47 of the 90 cases evaluated by the two indicators. When the proposed algorithm is compared with the weight-sum based algorithms, 83 out of 90 examples achieve the optimal performance.

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Section: The Background Of Maopsmentioning

confidence: 99%

Two-Population Coevolutionary Algorithm with Dynamic Learning Strategy for Many-Objective Optimization

Gui

Wang

Wang³

2021

Mathematics

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“…This method is used in MMOPSO [49]. Other representative algorithms in this category include DP-DMPPSO [50], dMOPSO [51], and D 2 MOPSO [52], etc. Because the fast convergence of PSO makes the particles easy to distribute on the boundary, this method is more effective for particle distribution.…”

Section: B Multi-objective Particle Swarm Optimizermentioning

confidence: 99%

A Competitive Particle Swarm Algorithm Based on Vector Angles for Multi-Objective Optimization

2021

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Recently, the particle swarm algorithm (PSO) has demonstrated its effectiveness in solving multi-objective optimization problems. However, the performance of most existing multi-objective particle swarm algorithms depends largely on the global or individual best particles. Moreover, due to the rapid convergence of PSO in single objective optimization problems, PSO is prone to poorly distributed indicators when dealing with multi-objective optimization problems. To solve the above problems, we propose a multi-objective competitive particle swarm algorithm based on vector angles (VaCSO). Firstly, in order to remove the influence of global best particles or individual best particles on the algorithm, the competition mechanism is used. Secondly, in order to increase the diversity of solutions while maintaining the convergence of the algorithm, the population is clustered into two populations. Population 1 mainly considers the convergence of the solution in the offspring generation strategy. As a supplement, population 2 adds a new offspring generation strategy to maintain the distribution of the solution, and we innovatively proposed a three-particle competition to improve the distribution and diversity of particle swarms. Finally, based on vector angle information, we consider auxiliary learning to optimize the population gap, so as to improve the distribution of the algorithm. We have established two sets of comparative experiments to test the performance of VaCSO. We compared VaCSO with the currently popular multi-objective particle swarm optimizers and multi-objective evolutionary algorithms. Experimental results show that VaCSO has an excellent performance in convergence and distribution, and has a significant effect in optimizing quality.INDEX TERMS multi-objective optimization, competition particle swarm, competition mechanism, threeparticle competition, vector angle information, auxiliary learning.

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“…In AgMOPSO [28], a novel decomposition approach is used in MOPSOs to select the personal-best particle and global-best particle during the evolutionary search process, while the Pareto dominance method is applied to update the external archive and particle swarms. In DP-DMOPSO [44], an external archive is adopted to reserve non-dominated solutions and a space decomposition-based mechanism is presented to renew the external archive. GSADMSPSO [45] was proposed by combining the dynamic multiple swarm particle optimization with gravitational search algorithm to enhance the ability of exploitation and exploration.…”

Section: Recent Studies On Mopsosmentioning

confidence: 99%

A Novel Hybrid Multi-Objective Particle Swarm Optimization Algorithm With an Adaptive Resource Allocation Strategy

Chen

Gong

et al. 2019

IEEE Access

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Recently, there are a number of particle swarm optimization algorithms (PSOs) proposed for tackling multi-objective optimization problems (MOPs). Most of multi-objective PSOs (MOPSOs) were designed to speed up their convergence, which have been validated when tackling various kinds of MOPs. However, they may face some challenges for tackling some complicated MOPs, such as the UF test problems with complicated Pareto-optimal sets, mainly due to their neglect on the diversity. To solve the above problem, a novel hybrid MOPSO (called HMOPSO-ARA) is suggested in this paper with an adaptive resource allocation strategy, which shows a superior performance over most MOPSOs. Using the decomposition approach in HMOPSO-ARA, MOPs are transferred into a set of subproblems, each of which is accordingly optimized by one particle using a novel velocity update approach with the strengthened search capability. Then, an adaptive resource allocation strategy is employed based on the relevant improvement on the aggregated function, which can reasonably assign the computational resource to the particles according to their performance, so as to accelerate the convergence speed to the true Pareto-optimal front. Moreover, a decomposition-based clonal selection strategy is further used to enhance our performance, where the cloning process is run on the external archive based on the relevant fitness improvement. The experiments validate the superiority of HMOPSO-ARA over four competitive MOPSOs (SMPSO, CMPSO, dMOPSO and AgMOPSO) and four competitive multi-objective evolutionary algorithms (MOEA/D-ARA, MOEA/D-DE MOEA/D-GRA and EF_PD) when tackling thirty-five test problems (DTLZ1-DTLZ9, WFG1-WFG9, UF1-UF10 and F1-F9), in terms of two widely used performance indicators.

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A dynamic multiple populations particle swarm optimization algorithm based on decomposition and prediction

Cited by 34 publications

References 28 publications

Two-Population Coevolutionary Algorithm with Dynamic Learning Strategy for Many-Objective Optimization

Two-Population Coevolutionary Algorithm with Dynamic Learning Strategy for Many-Objective Optimization

A Competitive Particle Swarm Algorithm Based on Vector Angles for Multi-Objective Optimization

A Novel Hybrid Multi-Objective Particle Swarm Optimization Algorithm With an Adaptive Resource Allocation Strategy

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