PESO+for Constrained Optimization

Zavala, Angel E. Muñoz; Hernández-Aguirre, Arturo; Villa-Diharce, Enrique; Botello-Rionda, Salvador

doi:10.1109/cec.2006.1688313

Cited by 25 publications

(13 citation statements)

References 7 publications

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“…Although the capability of ATM can be significantly improved by taking advantage of the shrinking space technique, compared with other methods such as [24,34], AATM still leaves plenty of room for improvement. When using EAs to deal with COPs, the search algorithm plays a very important role on the performance in addition to the constraint-handling technique.…”

Section: Discussionmentioning

confidence: 99%

Accelerating adaptive trade‐off model using shrinking space technique for constrained evolutionary optimization

Wang

Cai

Zhou

2008

Numerical Meth Engineering

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SUMMARYAdaptive trade-off model (ATM) is a constraint-handling mechanism proposed recently. The main advantages of this model are its simplicity and adaptation. Moreover, it can be easily embedded into evolutionary algorithms for solving constrained optimization problems. This paper proposes a novel method for constrained optimization, which aims at accelerating the ATM using shrinking space technique. Eighteen benchmark test functions and five engineering design problems are used to test the performance of the method proposed. Experimental results suggest that combining the ATM with the shrinking space technique is very beneficial. The method proposed can promptly converge to competitive results without loss of the quality and the precision of the final results. Performance comparisons with some other state-of-the-art approaches from the literature are also presented.

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Section: Discussionmentioning

confidence: 99%

Accelerating adaptive trade‐off model using shrinking space technique for constrained evolutionary optimization

Wang

Cai

Zhou

2008

Numerical Meth Engineering

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“…The PC (personal computer) configuration for this study is listed in Appendix B. In addition, as a rigorous basis of performance comparison, the proposed RCGA will compare with the following state-of-the-art algorithms: DE [61], DMS-PSO [62], ε-DE [47], GDE [63], jDE-2 [64], MDE [65], MPDE [66], PCX [67], PESO+ [68] and SaDE [69].…”

Section: E C mentioning

confidence: 99%

A simple and efficient real-coded genetic algorithm for constrained optimization

Chuang

Chen

Hwang

2016

Applied Soft Computing

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“…In addition, the objective and constraints are assigned to the sub-swarms adaptively according to the difficulties of the constraints. Another novel method called PESO+ (Particle Evolutionary Swarm Optimization Plus) is proposed by Munoz-Zavala et al [15]. Moreover, sequential quadratic programming (SQP) is used for local search.…”

Section: ½ áòøöó ù ø óòmentioning

confidence: 99%

“…Error values achieved when FFEs= 5×10 3 , FFEs= 5×10 4 , and FFEs= 5×10 5 for test functions[13][14][15][16][17][18] …”

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confidence: 99%

A hybrid multi-swarm particle swarm optimization to solve constrained optimization problems

Wang

Cai

2009

Front. Comput. Sci. China

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In the real-world applications, most optimization problems are subject to different types of constraints. These problems are known as constrained optimization problems (COPs). Solving COPs is a very important area in the optimization field. In this paper, a hybrid multi-swarm particle swarm optimization (HMPSO) is proposed to deal with COPs. This method adopts a parallel search operator in which the current swarm is partitioned into several subswarms and particle swarm optimization (PSO) is severed as the search engine for each sub-swarm. Moreover, in order to explore more promising regions of the search space, differential evolution (DE) is incorporated to improve the personal best of each particle. First, the method is tested on 13 benchmark test functions and compared with three stateof-the-art approaches. The simulation results indicate that the proposed HMPSO is highly competitive in solving the 13 benchmark test functions. Afterward, the effectiveness of some mechanisms proposed in this paper and the effect of the parameter setting were validated by various experiments. Finally, HMPSO is further applied to solve 24 benchmark test functions collected in the 2006 IEEE Congress on Evolutionary Computation (CEC2006) and the experimental results indicate that HMPSO is able to deal with 22 test functions.

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PESO+for Constrained Optimization

Cited by 25 publications

References 7 publications

Accelerating adaptive trade‐off model using shrinking space technique for constrained evolutionary optimization

Accelerating adaptive trade‐off model using shrinking space technique for constrained evolutionary optimization

A simple and efficient real-coded genetic algorithm for constrained optimization

A hybrid multi-swarm particle swarm optimization to solve constrained optimization problems

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