Orthogonal Learning Particle Swarm Optimization

Zhan, Zhi-Hui; Zhang, Mengjie; Li, Yun; Shi, Yuhui

doi:10.1109/tevc.2010.2052054

Cited by 650 publications

(181 citation statements)

References 51 publications

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“…In this section, comparisons of SGO versus OEA, HPSO-TVAC (Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients) [26], APSO (adaptive particle swarm optimization) [27], CLPSO (comprehensive learning particle swarm optimization) [28], OLPSO (orthogonal learning particle swarm optimization)-L [29] and OLPSO-G [29] on nine benchmarks listed in Appendix are carried out. OEA uses 3.0 × 10 5 FEs, HPSO-TVAC, CLPSO, APSO, OLPSO-L and OLPSO-G use 2.0×10 5 FEs, whereas SGO runs for 3 × 10 3 FEs for sphere, schwefel 1.2, schwefel 2.22 function, 1.0×10 2 FEs for step, 4.0×10 2 FEs for rastrigin, noncontinuous rastrigin and griwank, 1.0 × 10 3 FEs for Ackley and quartic function.…”

Section: Experiments 3: Sgo Vs Oea Hpso-tvac Clpso Apso Olpso-l Anmentioning

confidence: 99%

“…OEA uses 3.0 × 10 5 FEs, HPSO-TVAC, CLPSO, APSO, OLPSO-L and OLPSO-G use 2.0×10 5 FEs, whereas SGO runs for 3 × 10 3 FEs for sphere, schwefel 1.2, schwefel 2.22 function, 1.0×10 2 FEs for step, 4.0×10 2 FEs for rastrigin, noncontinuous rastrigin and griwank, 1.0 × 10 3 FEs for Ackley and quartic function. The results of OEA, HPSO-TVAC, CLPSO and APSO are gained from [28] and [27] directly, and for OLPSO-L and OLPSO-G, results are gained from [29] directly and put in Table 3. In Table, "NA" stands for experiment is not conducted for that particular function.…”

Section: Experiments 3: Sgo Vs Oea Hpso-tvac Clpso Apso Olpso-l Anmentioning

confidence: 99%

“…In this section, the comparison of SGO versus GPSO(global PSO) [45], LPSO(local PSO) [46], FIPS(fully informed particle swarm) [47],SPSO(standard for particle swarm optimization) [48], CLPSO [28], OLPSO [29] and CLP-SOA(scatter learning particle swarm optimization Algorithm) [49], on 14 benchmark functions described in paper [49]. The maximum number of fitness evaluations is taken as 200,000, and population size is 40, and the parameters of the algorithms are identical to [49].…”

Section: Experiments 7: Sgo Vs Saabc Gabc Iabc Abc/best1 Gpso Dbmentioning

confidence: 99%

See 2 more Smart Citations

Social group optimization (SGO): a new population evolutionary optimization technique

Satapathy¹,

Naik²

2016

Complex Intell. Syst.

218

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Social group optimization (SGO), a populationbased optimization technique is proposed in this paper. It is inspired from the concept of social behavior of human toward solving a complex problem. The concept and the mathematical formulation of SGO algorithm is explained in this paper with a flowchart. To judge the effectiveness of SGO, extensive experiments have been conducted on number of different unconstrained benchmark functions as well as standard numerical benchmark functions taken from the IEEE Congress on Evolutionary Computation 2005 competition. Performance comparisons are made between state-of-theart optimization techniques, like GA, PSO, DE, ABC and its variants, and the recently developed TLBO. The investigational outcomes show that the proposed social group optimization outperforms all the investigated optimization techniques in computational costs and also provides optimal solutions for most of the functions considered in our work. The proposed technique is found to be very simple and straightforward to implement as well. It is believed that SGO will supplement the group of effective and efficient optimization techniques in the population-based category and give researchers wide scope to choose this in their respective applications.

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Section: Experiments 3: Sgo Vs Oea Hpso-tvac Clpso Apso Olpso-l Anmentioning

confidence: 99%

Section: Experiments 3: Sgo Vs Oea Hpso-tvac Clpso Apso Olpso-l Anmentioning

confidence: 99%

Section: Experiments 7: Sgo Vs Saabc Gabc Iabc Abc/best1 Gpso Dbmentioning

confidence: 99%

See 1 more Smart Citation

Social group optimization (SGO): a new population evolutionary optimization technique

Satapathy¹,

Naik²

2016

Complex Intell. Syst.

218

View full text Add to dashboard Cite

show abstract

“…As is known, both selective pressure and population diversity are significant for EAs [35][36][37][38]. However, in the traditional DE, a one-to-one competition mechanism is utilized as the selection operation between each pair of the target vector and its corresponding trial vector, which may induce weak selective pressure owing to its unbiased selection of parents or target vectors [26].…”

Section: Motivationsmentioning

confidence: 99%

A Thermodynamical Selection-Based Discrete Differential Evolution for the 0-1 Knapsack Problem

Guo

Yue

Zhang

et al. 2014

Entropy

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Many problems in business and engineering can be modeled as 0-1 knapsack problems. However, the 0-1 knapsack problem is one of the classical NP-hard problems. Therefore, it is valuable to develop effective and efficient algorithms for solving 0-1 knapsack problems. Aiming at the drawbacks of the selection operator in the traditional differential evolution (DE), we present a novel discrete differential evolution (TDDE) for solving 0-1 knapsack problem. In TDDE, an enhanced selection operator inspired by the principle of the minimal free energy in thermodynamics is employed, trying to balance the conflict between the selective pressure and the diversity of population to some degree. An experimental study is conducted on twenty 0-1 knapsack test instances. The comparison results show that TDDE can gain competitive performance on the majority of the test instances.

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“…However, a common problem often experienced when applying PSO to multimodal function optimization is that the particle population loses diversity too rapidly before it converges to some reasonable solutions 3,4,5,6,7 , which is commonly referred as premature convergence.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimizer with Aging Operator for Multimodal Function Optimization

Jiang

Ning

2013

IJCIS

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This paper proposes a new scheme for preventing a Particle Swarm Optimizer from premature convergence on multimodal optimization problems. Instead of only using fitness evaluation, we use a new index called particle age to guide population towards more promising region of the search space. The particle age is a measure of how long each particle moves towards a better solution. The main novelty of the proposed method is to let each particle learn from not only neighbours with better fitness values but also the neighbours whose fitness values are updated more frequently. To achieve this, we design a comprehensive age-based learning strategy, in which age is used for excluding old particles, selecting learning exemplars and deciding mutation strength and inertial weight for each particle. Experiments were conducted on 15 multimodal test functions to assess the performance of this new strategy in comparison with 7 state-ofthe-art PSOs from the literature. The experimental results show the good performance of the proposed algorithm in solving multimodal functions when compared with several existing PSO variants.

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Orthogonal Learning Particle Swarm Optimization

Cited by 650 publications

References 51 publications

Social group optimization (SGO): a new population evolutionary optimization technique

Social group optimization (SGO): a new population evolutionary optimization technique

A Thermodynamical Selection-Based Discrete Differential Evolution for the 0-1 Knapsack Problem

Particle Swarm Optimizer with Aging Operator for Multimodal Function Optimization

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