Comparing evolutionary strategies on a biobjective cultural algorithm.

Carolina Lagos,José-Miguel Rubio,Fernando Paredes,Ricardo Soto,Broderick Crawford,Enrique Cabrera

doi:10.1155/2014/745921

Abstract

Evolutionary algorithms have been widely used to solve large and complex optimisation problems. Cultural algorithms (CAs) are evolutionary algorithms that have been used to solve both single and, to a less extent, multiobjective optimisation problems. In order to solve these optimisation problems, CAs make use of different strategies such as normative knowledge, historical knowledge, circumstantial knowledge, and among others. In this paper we present a comparison among CAs that make use of different evolutionary strategies; the first one implements a historical knowledge, the second one considers a circumstantial knowledge, and the third one implements a normative knowledge. These CAs are applied on a biobjective uncapacitated facility location problem (BOUFLP), the biobjective version of the well-known uncapacitated facility location problem. To the best of our knowledge, only few articles have applied evolutionary multiobjective algorithms on the BOUFLP and none of those has focused on the impact of the evolutionary strategy on the algorithm performance. Our biobjective cultural algorithm, called BOCA, obtains important improvements when compared to other well-known evolutionary biobjective optimisation algorithms such as PAES and NSGA-II. The conflicting objective functions considered in this study are cost minimisation and coverage maximisation. Solutions obtained by each algorithm are compared using a hypervolume S metric.

Highlights

Evolutionary algorithms (EAs) are an effective alternative to solve several large and complex optimisation problems, as they are able to find good solutions for a wide range of problems in acceptable computational time
A comparison between results obtained by well-known evolutionary multiobjective optimisation (EMO) algorithms such as NSGA-II and PAES and our biobjective cultural algorithm (BOCA) algorithm is presented
Results show that performance of the BOCA algorithm depends largely on the selected knowledge and it can make the difference in terms of S value, time, and number of efficient solutions found by the algorithm

Summary

Introduction

Evolutionary algorithms (EAs) are an effective alternative to (approximately) solve several large and complex optimisation problems, as they are able to find good solutions for a wide range of problems in acceptable computational time. They tend to fall into premature convergence with low evolution efficiency [8] This is because of implicit information embodied in the evolution process and domain knowledge corresponding to optimisation problems which are not fully included in the solution approach [9]. In the belief space, implicit knowledge is extracted from selected individuals in the population and stored in a different way They are used to guide the whole evolution process in the population space such that they can induce the population to escape from local optimal solutions. Zhang et al [14] present a CA which is enhanced by using a particle swarm optimisation algorithm This enhanced CA is applied to fuel distribution MO problem.

Overview

Computational Experiments

Conclusions and Future Work