Multiple Criteria Decision Making for Swarm Optimization

This book presents a new formulation for the swarmoptimization technique as a system of autonomousagents. The proposed technique is based on anintimate understanding of swarms and animalaggregations in an attempt to simulate cognitivethinking of their members. The dynamic balancebetween gregarious and social intolerance behaviorsdemonstrated by social animals is used to form theswarm and keep its persistence. In this work, membersof the swarm are represented by agents that enjoy acertain degree of freewill to respond adaptively tochanges on the states of their swarm mates. Adaptiveresponses are reflected on the way agents move insidethe problem domain. A new set of basic behaviors isdefined, namely imitation, memory retrieval,momentum, and play. A multi-criterion decision makingprocess (MCDM) is employed to update positions ofswarm members in the problem space. Decision makingalternatives are defined from the set of basicbehaviors. Fitness and diversity characterize thedecision criteria that are used to measure theperformance of each alternative. Levenshtien editdistance is used to measure the distance betweenagents in the genotype space. Criteria are thenstandardized by means of fuzzy sets. Fuzzified valuesof criteria are aggregated by the fuzzy orderedweighted average (OWA) to reach a single evaluationfunction. The overall decision making process ismade to promote both fitness and diversity. Theproposed technique is tested using the travelingsalesmen (TSP) and quadratic assignment problems(QAP). Results and comparisons show that thetechnique outperforms the traditional particle swarmoptimizer (PSO). Also, a comparison of the proposedtechnique with the standard genetic algorithm (SGA)shows that comparable results can be obtained. Anextension of the proposed technique is also proposedto solve optimization problems with continuousvariables. For this class of optimization, a largeset of diverse benchmark problems is used to test theproposed technique. A comparison of the performancewith the simple evolutionary algorithm SEA and manyother particle swarm variants is also carried out.Results show that the proposed technique outperformsother techniques included in the comparison in almostall the tested problems.

Autorentext

Iam a research associate in the school of Engineering, Dalhousie University, Canada. I have a PhD from Dalhousie University, Canada, for work in heuristic search and mathematical optimization (2007). I also have a PhD in Electrical and Computer Engineering from Suez Canal University, Egypt (2003). I was then an assistant professor at Suez Canal University. I received my B.Sc. and M.Sc. in Electrical and Computer Engineering from Suez Canal University, Egypt.

Klappentext
This book presents a new formulation for the swarm optimization technique as a system of autonomous agents. The proposed technique is based on an intimate understanding of swarms and animal aggregations in an attempt to simulate cognitive thinking of their members. The dynamic balance between gregarious and social intolerance behaviors demonstrated by social animals is used to form the swarm and keep its persistence. In this work, members of the swarm are represented by agents that enjoy a certain degree of freewill to respond adaptively to changes on the states of their swarm mates. Adaptive responses are reflected on the way agents move inside the problem domain. A new set of basic behaviors is defined, namely imitation, memory retrieval, momentum, and play. A multi-criterion decision making process (MCDM) is employed to update positions of swarm members in the problem space. Decision making alternatives are defined from the set of basic behaviors. Fitness and diversity characterize the decision criteria that are used to measure the performance of each alternative. Levenshtien edit distance is used to measure the distance between agents in the genotype space. Criteria are then standardized by means of fuzzy sets. Fuzzified values of criteria are aggregated by the fuzzy ordered weighted average (OWA) to reach a single evaluation function. The overall decision making process is made to promote both fitness and diversity. The proposed technique is tested using the traveling salesmen (TSP) and quadratic assignment problems (QAP). Results and comparisons show that the technique outperforms the traditional particle swarm optimizer (PSO). Also, a comparison of the proposed technique with the standard genetic algorithm (SGA) shows that comparable results can be obtained. An extension of the proposed technique is also proposed to solve optimization problems with continuous variables. For this class of optimization, a large set of diverse benchmark problems is used to test the proposed technique. A comparison of the performance with the simple evolutionary algorithm SEA and many other particle swarm variants is also carried out. Results show that the proposed technique outperforms other techniques included in the comparison in almost all the tested problems.