Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization

Liu, Hui; Cai, Zixing; Wang, Yong

doi:10.1016/j.asoc.2009.08.031

Cited by 621 publications

(212 citation statements)

References 23 publications

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“…Some of these studies hybridize differential evolution with biogeographybased optimization to solve global optimization problem [8]. Some hybridize particle swarm optimization with differential evolution for solving constrained numerical and engineering optimization problems [19]. Moreover, some of them hybridize chaotic with meta-heuristic algorithms for solving feature selection problem [33].…”

Section: B Gehad Ismail Sayedmentioning

confidence: 99%

“…The detailed description of the adopted engineering problem is presented in Appendix 2. Table 7 presents the optimal solution for three-bar truss design problem obtained by particle swarm optimization with differential evolution (PSODE) [20], dynamic stochastic selection (DEDS) [42], mine blast algorithm (MBA) [29], water cycle algorithm (WCA) [6], MFO and SA-MFO. As it can be seen from this table, SA-MFO obtained better results compared with the standard MFO.…”

Section: Comparison Using Engineering Design Problems Experimentsmentioning

confidence: 99%

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A hybrid SA-MFO algorithm for function optimization and engineering design problems

Sayed

Hassanien

2018

Complex Intell. Syst.

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This paper presents a hybrid algorithm based on using moth-flame optimization (MFO) algorithm with simulated annealing (SA), namely (SA-MFO). The proposed SA-MFO algorithm takes the advantages of both algorithms. It takes the ability to escape from local optima mechanism of SA and fast searching and learning mechanism for guiding the generation of candidate solutions of MFO. The proposed SA-MFO algorithm is applied on 23 unconstrained benchmark functions and four well-known constrained engineering problems. The experimental results show the superiority of the proposed algorithm. Moreover, the performance of SA-MFO is compared with well-known and recent meta-heuristic algorithms. The results show competitive results of SA-MFO concerning MFO and other meta-heuristic algorithms.

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Section: B Gehad Ismail Sayedmentioning

confidence: 99%

Section: Comparison Using Engineering Design Problems Experimentsmentioning

confidence: 99%

A hybrid SA-MFO algorithm for function optimization and engineering design problems

Sayed

Hassanien

2018

Complex Intell. Syst.

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“…The research result shows that the performance of the HPSO refines the optimization performance as compared to PSO, DE or GA algorithm. Liu et al (2010) have used the hybrid of DE and PSO algorithm for engineering optimization and constrained optimization problems. They have integrated the DE crossover and mutation in PSO that may help PSO to jump out of stagnation.…”

Section: Related Work In Hybrid Pso and Dementioning

confidence: 99%

OPSODE: Opposition based particle swarm optimization instilled with differential evolution

Аббас

2017

Int. j. adv. appl. sci.

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Particle Swarm Optimization (PSO) is a very powerful global optimization technique. Differential Evolution (DE) is another fast and emerging algorithm of evolutionary computing. PSODE is hybrid of PSO and DE that incorporates diversity in the PSO algorithm. In this research a new opposition based version of PSODE (OPSODE) is proposed that incorporates some more diversity by employing the opposition based learning in the PSODE algorithm. Some standard benchmark functions are used to access the performance of the OPOSDE algorithm. The proposed version is then compared with the PSO, OPSO, and PSODE algorithm. The research result shows that the new version OPSODE has significance performance.

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“…Togan et al [23] presented a design procedure employing the TLBO to the discrete optimization of planar steel frames. Yu et al [24] applied TLBO on several numerical and engineering optimization problems and proved that TLBO is more powerful than the improved bee algorithm (IBA) [25], the hybrid particle swarm optimization with differential evolution (PSO-DE) [26], the modified differential evolution algorithm (COMDE) [27], the g-best guided artificial bee colony (GABC) [28] and the upgraded artificial bee colony (UABC) [29] algorithms.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection Based on Multi-Scale Local Binary Patterns Operator and Improved Teaching-Learning-Based Optimization Algorithm

Zhang

Yang

2015

Symmetry

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Aiming to effectively recognize train center plate bolt loss faults, this paper presents an improved fault detection method. A multi-scale local binary pattern operator containing the local texture information of different radii is designed to extract more efficient discrimination information. An improved teaching-learning-based optimization algorithm is established to optimize the classification results in the decision level. Two new phases including the worst recombination phase and the cuckoo search phase are incorporated to improve the diversity of the population and enhance the exploration. In the worst recombination phase, the worst solution is updated by a crossover recombination operation to prevent the premature convergence. The cuckoo search phase is adopted to escape the local optima. Experimental results indicate that the recognition accuracy is up to 98.9% which strongly demonstrates the effectiveness and reliability of the proposed detection method.

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Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization

Cited by 621 publications

References 23 publications

A hybrid SA-MFO algorithm for function optimization and engineering design problems

A hybrid SA-MFO algorithm for function optimization and engineering design problems

OPSODE: Opposition based particle swarm optimization instilled with differential evolution

Fault Detection Based on Multi-Scale Local Binary Patterns Operator and Improved Teaching-Learning-Based Optimization Algorithm

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