On Mixed-Discrete Nonlinear Optimization Problems: A Comparative Study

Lin, Shui-Shun; Zhang, Chun; Wang, Hsu‐Pin

doi:10.1080/03052159508941359

Cited by 38 publications

(12 citation statements)

References 10 publications

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“…Sandgren [24] Loh and Papalambros [22] Zhang and Wang [29] Lin et al [20] Guo et al [12] PSO of Kennedy and Eberthart [16] Proposed PSO and the discrete-valued spring wire diameter (d). Accordingly, the optimization problem is formulated as below:…”

Section: Variables/ Functionsmentioning

confidence: 98%

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A real-integer-discrete-coded particle swarm optimization for design problems

Datta

Figueira

2011

Applied Soft Computing

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Section: Variables/ Functionsmentioning

confidence: 98%

“…Simulated annealing algorithm [29]. Genetic algorithm [10,20]. Hybrid swarm intelligence approach [12].…”

Section: Problemmentioning

confidence: 99%

A real-integer-discrete-coded particle swarm optimization for design problems

Datta

Figueira

2011

Applied Soft Computing

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“…Z represents integer set, and Table 5 shows the comparison results. In this table, we use the previous work in comparisons [28][29][30][31][32][33][34]. It is obvious that the results in paper [30] and SCEA have the same best performances.…”

Section: Design a Gear Trainmentioning

confidence: 99%

Species co-evolutionary algorithm: a novel evolutionary algorithm based on the ecology and environments for optimization

Wang

Cai

et al. 2015

Neural Comput & Applic

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In classic evolutionary algorithms (EAs), solutions communicate each other in a very simple way so the recombination operator design is simple, which is easy in algorithms' implementation. However, it is not in accord with nature world. In nature, the species have various kinds of relationships and affect each other in many ways. The relationships include competition, predation, parasitism, mutualism and pythogenesis. In this paper, we consider the five relationships between solutions to propose a co-evolutionary algorithm termed species co-evolutionary algorithm (SCEA). In SCEA, five operators are designed to recombine individuals in population. A set including several classical benchmarks are used to test the proposed algorithm. We also employ several other classical EAs in comparisons. The comparison results show that SCEA exhibits an excellent performance to show a huge potential of SCEA in optimization.

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“…The threshold  is set Fig. 6 shows a simplified configuration of a compound gear train that has been investigated by many researchers [23][24][25][26][27][28]. It is desired to produce a gear ratio, or velocity ratio, as close as possible to 1/6.931.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…Table 3 Comparison of results for the pressure vessel problem. (23) Zhang (25) Wu (26) Lin (27) Guo (28) Proposed Method He (30) Kitayama (12) …”

Section: Optimum Design Of Pressure Vesselmentioning

confidence: 99%

Discrete Differential Evolution for Mixed Discrete Non-Linear Problems

Kitayama¹,

Arakawa²,

Yamazaki³

2012

JCEA

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Differential evolution (DE) is a global optimizer for continuous design variables. To enhance DE, it is necessary to handle discrete design variables. In this paper, a discrete differential evolution (DDE) algorithm is proposed to handle discrete design variables. The proposed DDE is based on the DE/1/rand/bin method. In the proposed DDE, the mutation ratio is regarded as the exchange probability, and thus, no modifications of DE/1/rand/bin are required. In addition, in order to maintain diversity through the search process, we initialize all search points. By introducing the initialization of all search points, global or quasi-optimum solution can be found. We validate the proposed DDE by applying it to several benchmark problems.

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On Mixed-Discrete Nonlinear Optimization Problems: A Comparative Study

Cited by 38 publications

References 10 publications

A real-integer-discrete-coded particle swarm optimization for design problems

A real-integer-discrete-coded particle swarm optimization for design problems

Species co-evolutionary algorithm: a novel evolutionary algorithm based on the ecology and environments for optimization

Discrete Differential Evolution for Mixed Discrete Non-Linear Problems

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