Balancing convergence and diversity plays a key role in evolutionary multiobjective
optimization (EMO). Most current EMO algorithms perform well on problems with two or three …

Managing convergence and diversity is essential in the design of multiobjective particle
swarm optimization (MOPSO) in search of an accurate and well distributed approximation of …

The optimal solutions of a multiobjective optimization problem correspond to a
nondominated front that is characterized by a tradeoff between objectives. A knee region in …

Recently, by taking advantage of evolutionary multiobjective optimization techniques in
diversity preservation, the means of multiobjectivization has attracted increasing interest in …

This paper proposes a grid-guided particle swarm optimizer for solving multimodal multi-
objective optimization problems that may have multiple disjoint Pareto sets corresponding to …

Cooperative coevolutionary (CC) algorithms decompose a problem into several
subcomponents and optimize them separately. Such a divide-and-conquer strategy makes …

Evolutionary multi-objective optimization (EMO) algorithms are actively used for answering
optimization problems with multiple contradictory objectives and scheming interpretable and …

List of References on Evolutionary Multiobjective Optimization Page 1 List of References on
Evolutionary Multiobjective Optimization Carlos A. Coello Coello ccoello@cs.cinvestav.mx …

The successful application of multi-objective evolutionary algorithms (MOEAs) in many kinds
of multiobjective problems have attracted considerable attention in recent years. In this …

Convergence and diversity of solutions play an essential role in the design of multi-objective
evolutionary algorithms (MOEAs). Among the available diversity mechanisms, the …