MULTIOBJECTIVE OPTIMIZATION AND MULTIPLE CONSTRAINT HANDLING WITH EVOLUTIONARY ALGORITHMS - PART I - A UNIFIED FORMULATION

In optimization, multiple objectives and constraints cannot be handled independently of the underlying optimizer, Requirements such as conti nuity and differentiability of the cost surface add yet another confli cting element to the decision process, While ''better'' solutions shou ld be rated higher than ''worse'' ones, the resulting cost landscape m ust also comply with such requirements, Evolutionary algorithms (EA's) , which have found application in many areas not amenable to optimizat ion by other methods, possess many characteristics desirable in a mult iobjective optimizer, most notably the concerted handling of multiple candidate solutions, However, EA's are essentially unconstrained searc h techniques which require the assignment of a scalar measure of quali ty, or fitness, to such candidate solutions, After reviewing current e volutionary approaches to multiobjective and constrained optimization, the paper proposes that fitness assignment be interpreted as, or at l east related to, a multicriterion decision process, A suitable decisio n making framework based on goals and priorities is subsequently formu lated in terms of a relational operator, characterized, and shown to e ncompass a number of simpler decision strategies, Finally, the ranking of an arbitrary number of candidates is considered, The effect of pre ference changes on the cost surface seen by an EA is illustrated graph ically for a simple problem, The paper concludes with the formulation of a multiobjective genetic algorithm based on the proposed decision s trategy, Niche formation techniques are used to promote diversity amon g preferable candidates, and progressive articulation of preferences i s shown to be possible as long as the genetic algorithm can recover fr om abrupt changes in the cost landscape.