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 …

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 …

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

Complexity in solving real‐world multicriteria optimization problems often stems from the fact
that complex, expensive, and/or time‐consuming simulation tools or physical experiments …

Evolutionary multi-objective optimization (EMO) may be computationally quite demanding,
because instead of searching for a single optimum, one generally wishes to find the whole …

We propose a new scalarization technique for nonconvex multiobjective optimization
problems and establish its theoretical properties. By combining our new scalarization …

As exact algorithms are unfeasible to solve real optimization problems, due to their
computational complexity, meta-heuristics are usually used to solve them. However …

In this paper, we present AMS-DEMO, an asynchronous master-slave implementation of
DEMO, an evolutionary algorithm for multi-objective optimization. AMS-DEMO was designed …

Proposes a divided-range multi-objective genetic algorithm (DRMOGA), which is a model for
the parallel processing of genetic algorithms (GAs) for multi-objective problems. In the …

This paper presents the parallel single front genetic algorithm (PSFGA), a parallel Pareto-
based algorithm for multiobjective optimization problems based on an evolutionary …