In population-based optimization algorithms (POAs), given an optimization problem, the
quality of the solutions depends heavily on the selection of algorithms, strategies and …

Recently, numerous multiobjective evolutionary algorithms (MOEAs) have been proposed to
solve the multiobjective optimization problems (MOPs). One of the most widely studied …

Decomposition-based multiobjective evolutionary algorithms (MOEAs) decompose a
multiobjective optimization problem into a set of scalar objective subproblems and solve …

Maintaining a balance between convergence and diversity is a challenge for multiobjective
evolutionary optimization. As crossover operators can affect the offspring distribution, an …

Multiobjective optimization entails minimizing or maximizing multiple objective functions
subject to a set of constraints. Many real world applications can be formulated as multi …

This paper reviews some state-of-the-art hybrid multiobjective evolutionary algorithms
(MOEAs) dealing with multiobjective optimization problem (MOP). The mathematical …

Over the past few years, differential evolution (DE) is generally considered as a reliable,
accurate and robust population-based evolutionary algorithm (EA). It is capable of handling …

In the last two decades, multiobjective optimization has become main stream and various
multiobjective evolutionary algorithms (MOEAs) have been suggested in the field of …

In this paper, a novel multi-objective grey wolf optimizer (MOGWO) based on multiple search
strategies (ie, adaptive chaotic mutation strategy, boundary mutation strategy, and elitism …

In this paper, we propagate the use of a set-based Newton method that enables computing a
finite size approximation of the Pareto front (PF) of a given twice continuously differentiable …