Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has
achieved great success in the field of evolutionary multi-objective optimization and has …

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 …

In this paper, a new multiobjective optimization framework based on nondominated sorting
and local search (NSLS) is introduced. The NSLS is based on iterations. At each iteration …

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 …

Invasive Weed Optimization (IWO) was recently proposed as a simple but powerful
metaheuristic algorithm for real parameter optimization. IWO draws inspiration from the …

Water is a critically scarce resource for industrial production and social life. The discharge of
untreated wastewater into natural waters can pose a serious risk to human health. Most of …

Decomposition is an efficient and prevailing strategy for solving multiobjective optimization
problems (MOPs). Its success has been witnessed by the multiobjective evolutionary …

Today, many complex multiobjective problems are dealt with using genetic algorithms (GAs).
They apply the evolution mechanism of a natural population to a “numerical” population of …