Recently, several meta-heuristics and evolutionary algorithms have been proposed for
tackling optimization problems. Such methods tend to suffer from degraded performance …

This work presents a multi-objective optimization method based on the artificial bee colony,
called the MOABC, for optimizing problems with multiple objectives. The MOABC uses a grid …

Traditional multiobjective evolutionary algorithms (MOEAs) consider multiple objectives as a
whole when solving multiobjective optimization problems (MOPs). However, this …

Assembly lines are useful for mass production of standard as well as customized products.
Line balancing is an important issue, in this regard an optimal or near optimal balance can …

In this paper, a Gaussian Bare-bones multi-objective Imperialist Competitive Algorithm
(GBICA) and its Modified version (MGBICA) are presented for the optimal electric power …

In addition to dominance-based and decomposition-based algorithms, performance
indicator-based algorithms have been widely used and investigated in the field of …

This paper presents an efficient multi-objective improved teaching–learning based
optimization (MO-ITLBO) algorithm for solving multi-objective optimization problems. The …

In this work, we explore a novel multi-objective optimization algorithm to identify a set of
solutions that could be optimal for more than one task. The proposed approach is used to …

Application problems have conflicting objectives and constraints, and maximum number of
generations is the most common termination criterion in evolutionary algorithms used for …

This study investigates a multi-objective Vortex Search algorithm (MOVS) by modifying the
single-objective Vortex Search algorithm or VS. The VS is a metaheuristic-based algorithm …