In almost no other field of computer science, the idea of using bio-inspired search paradigms
has been so useful as in solving multiobjective optimization problems. The idea of using a …

When attempting to better understand the strengths and weaknesses of an algorithm, it is
important to have a strong understanding of the problem at hand. This is true for the field of …

Having developed multiobjective optimization algorithms using evolutionary optimization
methods and demonstrated their niche on various practical problems involving mostly two …

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 …

This article gives a comprehensive introduction into one of the main branches of
evolutionary computation–the evolution strategies (ES) the history of which dates back to the …

After adequately demonstrating the ability to solve different two-objective optimization
problems, multiobjective evolutionary algorithms (MOEAs) must demonstrate their efficacy in …

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 …

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

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 …