Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding terms computational resources. Parallel implementations MOEAs (pMOEAs) provide considerable gains regarding performance and scalability and, therefore, their relevance tackling computation...

Over the past few years, the research on evolutionary algorithms has demonstrated their niche in solving multi-objective optimization problems, where the goal is to find a number of Pareto-optimal solutions in a single simulation run. Many studies have depicted different ways evolutionary algorithms can progress towards the true Paretooptimal solutions with a widely spread distribution of solut...

Large-scale multi-objective optimization problems (LS-MOP) are complex problems with a large number of decision variables. Due to its high-dimensional decision space, LS-MOP poses a significant challenge to multi-objective optimization methods including multiobjective evolutionary algorithms (MOEAs). Following the algorithmic framework of multiobjective evolutionary algorithm based on decomposi...

Solving Multi-objective Combinatorial Optimization Problems (MCOPs) is often a twofold problem: Firstly, the feasible region has to be identified in order to, secondly, improve the set of non-dominated solutions. In particular, problems where the construction of a single feasible solution is NPcomplete are most challenging. In the present paper, we will propose a combination of Multi-Objective ...

This paper has been worked on a RAP with multi-state components and the performance rate of each component working state may increase by spending technical and organizational activities costs. Whereas RAP belongs to Np-Hard problems, we used Genetic algorithm (GA) and simulated annealing (SA) and for solving the presented problem and calculating system reliability universal generating function ...

Multi-objective evolutionary algorithm (MOEAs) are well established population based stochastic techniques. They employ various evolutionary operators and approximate a set of optimal solutions for the problem at hand in single run unlike traditional mathematical programing. Each search operator have certain benefits and limitations. A multiple use of operators with self-adaptive procedure can ...