A modified particle swarm optimization for large-scale numerical optimizations and engineering design problems

Liu, Hao; Wang, Yue; Tu, Liangping; Ding, Guiyan; Hu, Yuhan

doi:10.1007/s10845-018-1403-1

Cited by 22 publications

(10 citation statements)

References 68 publications

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“…In order to simulate the disruption phenomenon, individuals in the population Pop with the same GR value are treated as one group, and different groups have different disruption conditions. It is different from Liu et al [ 11 ], Sarafrazi et al [ 34 ], and Ding et al [ 35 ]. All individuals are treated as one group.…”

Section: The Proposed Algorithmcontrasting

confidence: 56%

“…Cau refers to the disruption operator which is a matrix consisting of a set of Cauchy random numbers. It is worth noting that different dimensions of individual j have different Cauchy random numbers, which is different from Liu et al [ 11 ]. All dimensions of individual j have the same cauchyrnd.…”

Section: The Proposed Algorithmmentioning

confidence: 74%

“…Inspired by the disruption phenomenon of astrophysics, a novel operator named “Disruption” and its variants are introduced into single objective evolutionary algorithms [ 11 , 34 , 35 ]. In this paper, a layered disruption method is integrated into a multiobjective equilibrium optimization algorithm to enhance its exploration and exploitation abilities.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…The disruption phenomenon is originated from astrophysics [ 36 ]. As shown in Figure 2 , the swarm will be torn apart when the following condition is satisfied [ 11 ]:

where R is the distance between the center of mass of the swarm m and the mass M , and r represents the radius of the swarm m .…”

Section: The Proposed Algorithmmentioning

confidence: 99%

“…Research on optimization algorithms achieves rapid development due to the emergence of metaheuristic optimization algorithms. Many nature-inspired optimization algorithms have been proposed in the past few decades, including particle swarm optimization (PSO) [ 1 ], ant colony optimization (ACO) [ 2 ], evolution strategies (ES) [ 3 ], genetic algorithm (GA) [ 4 ], artificial bee colony algorithm (ABC) [ 5 ], gravitational search algorithm (GSA) [ 6 ], bat algorithm (BA) [ 7 ], flower pollination algorithm (FPA) [ 8 ], grey wolf optimizer (GWO) [ 9 ], whale optimization algorithm (WOA) [ 10 ], disruption particle swarm optimization (DPSO) [ 11 ], and equilibrium optimization algorithm (EO) [ 12 ]. Most of them are used to handle single objective optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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Disruption-Based Multiobjective Equilibrium Optimization Algorithm

Chen

Cui

2020

Computational Intelligence and Neuroscience

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Nature-inspired computing has attracted huge attention since its origin, especially in the field of multiobjective optimization. This paper proposes a disruption-based multiobjective equilibrium optimization algorithm (DMOEOA). A novel mutation operator named layered disruption method is integrated into the proposed algorithm with the aim of enhancing the exploration and exploitation abilities of DMOEOA. To demonstrate the advantages of the proposed algorithm, various benchmarks have been selected with five different multiobjective optimization algorithms. The test results indicate that DMOEOA does exhibit better performances in these problems with a better balance between convergence and distribution. In addition, the new proposed algorithm is applied to the structural optimization of an elastic truss with the other five existing multiobjective optimization algorithms. The obtained results demonstrate that DMOEOA is not only an algorithm with good performance for benchmark problems but is also expected to have a wide application in real-world engineering optimization problems.

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Section: The Proposed Algorithmcontrasting

confidence: 56%

Section: The Proposed Algorithmmentioning

confidence: 74%

Section: The Proposed Algorithmmentioning

confidence: 99%

“…The disruption phenomenon is originated from astrophysics [ 36 ]. As shown in Figure 2 , the swarm will be torn apart when the following condition is satisfied [ 11 ]:

where R is the distance between the center of mass of the swarm m and the mass M , and r represents the radius of the swarm m .…”

Section: The Proposed Algorithmmentioning

confidence: 99%