Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation

Lynn, Nandar; Suganthan, Ponnuthurai Nagaratnam

doi:10.1016/j.swevo.2015.05.002

Cited by 437 publications

(153 citation statements)

References 30 publications

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“…Hence, we propose a similarity selection (SS) rule, as given in Algorithm 2 (lines [11][12][13][14][15][16][17][18][19][20][21]. The rule simultaneously considers the fitness ranking information rank(i) of current solution xi and its Euclidian distance disti m to each of the M candidates yi m , which is defined as…”

Section: Similarity Selection Rulementioning

confidence: 99%

Selective-candidate framework with similarity selection rule for evolutionary optimization

Zhang

Chan

Peng

et al. 2020

Swarm and Evolutionary Computation

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Achieving better exploitation and exploration capabilities (EEC) have always been an important yet challenging issue in evolutionary optimization algorithm (EOA) design. The difficulties lie in obtaining a good balance in EEC, which is cooperatively determined by operations and parameters in an EOA. When deficiencies in exploitation or exploration are observed, most existing works only consider supplementing it, either by designing new operations or by altering the parameters. Unfortunately, when different situations are encountered, these proposals may fail to be the winner. To address these problems, this paper proposes an explicit EEC control method named selective-candidate framework with similarity selection rule (SCSS). On the one hand, M (M > 1) candidates are generated from each current solution with independent operations and parameters to enrich the search. While on the other hand, a similarity selection rule is designed to determine the final candidate. By considering the fitness ranking of the current solution and its Euclidian distance to each of these M candidates, superior current solutions select the closest to be the final candidate for efficient local exploitation while inferior ones would favor the farthest candidate for exploration purpose.In this way, the rule is able to synthesize exploitation and exploration, making the evolution more effective.The proposed SCSS framework is general and easy to implement. It has been applied to three classic, four state-of-the-art and four up-to-date EOAs from the branches of differential evolution, evolution strategy and particle swarm optimization. As confirmed with simulation results, significant performance enhancement is achieved.

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Section: Similarity Selection Rulementioning

confidence: 99%

Selective-candidate framework with similarity selection rule for evolutionary optimization

Zhang

Chan

Peng

et al. 2020

Swarm and Evolutionary Computation

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“…In this section, the performance of TPHS is compared with some state‐of‐the‐art metaheuristic algorithms for continuous optimization like CMA‐ES, comprehensive learning PSO (CL‐PSO), adaptive PSO (APSO), dynamic neighborhood learning PSO (DNL‐PSO), heterogeneous CL‐PSO (HCL‐PSO), social learning PSO (SL‐PSO), self‐regulating PSO (SR‐PSO), SSO algorithm, DE (DERand1Bin), DE with successful‐parent‐selecting framework (DERand1Bin‐SPS), and dynamic multiswarm particle swarm optimizer with HS (DMS‐PSO‐HS) on 30 dimensional IEEE CEC 2014 benchmark problems per the guidelines laid down in the benchmark suite; the results are reported in Tables and . The parameter setting adopted for all competing algorithms is same as given in the respective reference.…”

Section: Numerical Experimentsmentioning

confidence: 99%

A two‐phase harmony search algorithm for continuous optimization

Assad

Deep

2017

Computational Intelligence

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Harmony search (HS) algorithm is inspired by the music improvisation process in which a musician searches for the best harmony and continues to polish the harmony to improve its aesthetics. The efficiency of evolutionary algorithms depends on the extent of balance between diversification and intensification during the course of the search. An ideal evolutionary algorithm must have efficient exploration in the beginning and enhanced exploitation toward the end. In this paper, a two‐phase harmony search (TPHS) algorithm is proposed that attempts to strike a balance between exploration and exploitation by concentrating on diversification in the first phase using catastrophic mutation and then switches to intensification using local search in the second phase. The performance of TPHS is analyzed and compared with 4 state‐of‐the‐art HS variants on all the 30 IEEE CEC 2014 benchmark functions. The numerical results demonstrate the superiority of the proposed TPHS algorithm in terms of accuracy, particularly on multimodal functions when compared with other state‐of‐the‐art HS variants; further comparison with state‐of‐the‐art evolutionary algorithms reveals excellent performance of TPHS on composition functions. Composition functions are combined, rotated, shifted, and biased version of other unimodal and multimodal test functions and mimic the difficulties of real search spaces by providing a massive number of local optima and different shapes for different regions of the search space. The performance of the TPHS algorithm is also evaluated on a real‐life problem from the field of computer vision called camera calibration problem, ie, a 12‐dimensional highly nonlinear optimization problem with several local optima.

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“…When particles in a swarm use different mechanisms for choosing their informers, we say that the swarm exhibits model-of-influence heterogeneity. It was shown in [3], [4] that the Heterogeneous PSO (HPSO) model produced significantly better solutions than a selection of homogeneous PSO models.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Heterogeneous Particle Swarm Optimization

Yang

Sato

2017

IEICE Trans. Inf. & Syst.

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SUMMARYRecently, the Static Heterogeneous Particle Swarm Optimization (SHPSO) has been studied by more and more researchers. In SHPSO, the different search behaviours assigned to particles during initialization do not change during the search process. As a consequence of this, the inappropriate population size of exploratory particles could leave the SHPSO with great difficulties of escaping local optima. This motivated our attempt to improve the performance of SHPSO by introducing the dynamic heterogeneity. The self-adaptive heterogeneity is able to alter its heterogeneous structure according to some events caused by the behaviour of the swarm. The proposed triggering events are confirmed by keeping track of the frequency of the unchanged global best position (p g ) for a number of iterations. This information is then used to select a new heterogeneous structure when p g is considered stagnant. According to the different types of heterogeneity, DHPSO-d and DHPSO-p are proposed in this paper. In, particles dynamically use different rules for updating their position when the triggering events are confirmed. In DHPSO-p, a global gbest model and a pairwise connection model are automatically selected by the triggering configuration. In order to investigate the scalability of and DHPSO-p, a series of experiments with four state-of-the-art algorithms are performed on ten well-known optimization problems. The scalability analysis of and DHPSO-p reveals that the dynamic self-adaptive heterogeneous structure is able to address the exploration-exploitation trade-off problem in PSO, and provide the excellent optimal solution of a problem simultaneously. key words: Particle Swarm Optimization (PSO), dynamic heterogeneity, the gbest topology, the pairwise connection topology

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Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation

Cited by 437 publications

References 30 publications

Selective-candidate framework with similarity selection rule for evolutionary optimization

Selective-candidate framework with similarity selection rule for evolutionary optimization

A two‐phase harmony search algorithm for continuous optimization

Dynamic Heterogeneous Particle Swarm Optimization

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