Two-phase shuffled frog-leaping algorithm

Shuffled frog-leaping algorithm (SFLA) is a recent addition to the stochastic search methods that mimics the social and natural behaviour of species. The basic idea behind modelling of such algorithms is to achieve comparatively better solutions to the multifaceted optimization problems that are not easy to solve using traditional or deterministic mathematical techniques. SFLA combines the advantages of particle swarm optimization (PSO) and genetic algorithm (GA). In this study to improve the convergence speed, two modifications have been proposed firstly, initial population is generated using opposition based learning and secondly search process of SFLA is improved using scaling factor. The proposed algorithm is named as Two-Phase SFLA. The impact of the proposal is illustrated on four structural engineering design problems.

[1]  Muzaffar Eusuff,et al.  Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization , 2006 .

[2]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[3]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[4]  Singiresu S. Rao.  Engineering Optimization : Theory and Practice , 2010 .

[5]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  Kevin E Lansey,et al.  Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm , 2003 .

[8]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[9]  Guang-Yu Zhu,et al.  An improved Shuffled Frog-leaping Algorithm to optimize component pick-and-place sequencing optimization problem , 2014, Expert Syst. Appl..

[10]  K. Vaisakh,et al.  MSFLA/GHS/SFLA-GHS/SDE algorithms for economic dispatch problem considering multiple fuels and valve point loadings , 2013, Appl. Soft Comput..

[11]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[12]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[13]  D. M. Vinod Kumar,et al.  Generation bidding strategy in a pool based electricity market using Shuffled Frog Leaping Algorithm , 2014, Appl. Soft Comput..

[14]  J. Kiefer,et al.  Sequential minimax search for a maximum , 1953 .

[15]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[16]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[17]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[18]  Eskandar Gholipour,et al.  Decreasing activity cost of a distribution system company by reconfiguration and power generation control of DGs based on shuffled frog leaping algorithm , 2014 .

[19]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .