Memetic Algorithms for Multiobjective Optimization: Issues, Methods and Prospects

The concept of optimization—finding the extrema of a function that maps candidate ‘solutions’ to scalar values of ‘quality’—is an extremely general and useful idea that can be, and is, applied to innumerable problems in science, industry, and commerce. However, the vast majority of ‘real’ optimization problems, whatever their origins, comprise more than one objective; that is to say, ‘quality’ is actually a vector, which may be composed of such distinct attributes as cost, performance, profit, environmental impact, and so forth, which are often in mutual conflict. Until relatively recently this uncomfortable truth has been (wilfully?) overlooked in the sciences dealing with optimization, but now, increasingly, the idea of multiobjective optimization is taking over the centre ground. Multiobjective optimization takes seriously the fact that in most problems the different components that describe the quality of a candidate solution cannot be lumped together into one representative, overall measure, at least not easily, and not before some understanding of the possible ‘tradeoffs’ available has been established. Hence a multiobjective optimization algorithm is one which deals directly with a vector objective function and seeks to find multiple solutions offering different, optimal tradeoffs of the multiple objectives. This approach raises several unique issues in optimization algorithm design, which we consider in this article, with a particular focus on memetic algorithms (MAs). We summarize much of the relevant literature, attempting to be inclusive of relatively unexplored ideas, highlight the most important considerations for the design of multiobjective MAs, and finally outline our visions for future research in this exciting area.