Synchronous self-learning Pareto strategy An ensemble framework for vector and multi-criterion optimization  

作　　者：Ahmad Mozaffari 

机构地区：[1]Systems Design Engineering Department,University of Waterloo,Waterloo,Canada

出　　处：《International Journal of Intelligent Computing and Cybernetics》2018年第2期197-233,共37页智能计算与控制论国际期刊（英文）

摘　　要：Purpose–In recent decades,development of effective methods for optimizing a set of conflicted objective functions has been absorbing an increasing interest from researchers.This refers to the essence of real-life engineering systems and complex natural mechanisms which are generally multi-modal,non-convex and multi-criterion.Until now,several deterministic and stochastic methods have been proposed to cope with such complex systems.Advanced soft computational methods such as evolutionary games(cooperative and non-cooperative),Pareto-based techniques,fuzzy evolutionary methods,cooperative bio-inspired algorithms and neuro-evolutionary systems have effectively come to the aid of researchers to build up efficient paradigms with application to vector optimization.The paper aims to discuss this issue.Design/methodology/approach–A novel hybrid algorithm called synchronous self-learning Pareto strategy(SSLPS)is presented for the sake of vector optimization.The method is the ensemble of evolutionary algorithms(EA),swarm intelligence(SI),adaptive version of self-organizing map(CSOM)and a data shuffling mechanism.EA are powerful numerical optimization algorithms capable of finding a global extreme point over a wide exploration domain.SI techniques(the swarm of bees in our case)can improve both intensification and robustness of exploration.CSOM network is an unsupervised learning methodology which learns the characteristics of non-dominated solutions and,thus,enhances the quality of the Pareto front.Findings–To prove the effectiveness of the proposed method,the authors engage a set of well-known benchmark functions and some well-known rival optimization methods.Additionally,SSLPS is employed for optimal design of shape memory alloy actuator as a nonlinear multi-modal real-world engineering problem.The experiments show the acceptable potential of SSLPS for handling both numerical and engineering multi-objective problems.Originality/value–To the author’s best knowledge,the proposed algorithm is among the rare multiob

关 键 词：Self-organizing map Swarm and evolutionary computation Unsupervised machine assisted optimization Vector optimization 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]