Solving optimization problems with multiple (often conflicting) objectives is, generally, a very
difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the …

This paper gives an overview of meta-heuristics methods utilized within the paradigm of
multi-objective programming. This is an area of research that has undergone substantial …

Problems with multiple objectives arise in a natural fashion in most disciplines and their
solution has been a challenge to researchers for a long time. Despite the considerable …

In this paper, we provide a systematic comparison of various evolutionary approaches to
multiobjective optimization using six carefully chosen test functions. Each test function …

Many real-world problems involve two types of problem difficulty: i) multiple, conflicting
objectives and ii) a highly complex search space. On the one hand, instead of a single …

We introduce a simple evolution scheme for multiobjective optimization problems, called the
Pareto Archived Evolution Strategy (PAES). We argue that PAES may represent the simplest …

Although computational techniques for solving Multiobjective Optimization Problems (MOPs)
have been available for many years, the recent application of Evolutionary Algorithms (EAs) …

We describe a new selection technique for evolutionary multiobjective optimization
algorithms in which the unit of selection is a hyperbox in objective space. In this technique …

Most popular evolutionary algorithms for multiobjective optimisation maintain a population of
solutions from which individuals are selected for reproduction. In this paper, we introduce a …

In this paper, we study the problem features that may cause a multi-objective genetic
algorithm (GA) difficulty in converging to the true Pareto-optimal front. Identification of such …