Abstract—In this paper, a hybrid multiobjective evolutionary algorithm, MOEA/D-GO (Multiobjective Evolutionary Algorithm Based on Decomposition combined with Enhanced Genetic Operators), is proposed for fragment-type antenna design. It combines the ability and efficiency of MOEA/D to deal with multiobjective optimization problems with the specific character of two-dimensional chromosome coding ...

1. Abstract While various multiobjective optimization methods based on metaheuristic techniques have been proposed, these methods still encounter difficulties when handling many variables, or numerous objectives and constraints. This paper proposes a new aggregative gradient-based multiobjective optimization method for obtaining a Pareto-optimal solution set. In this method, the objective funct...

In this paper‎, ‎we introduce and study some new single-valued gap functions for non-differentiable semi-infinite multiobjective optimization problems with locally Lipschitz data‎. ‎Since one of the fundamental properties of gap function for optimization problems is its abilities in characterizing the solutions of the problem in question‎, ‎then the essential properties of the newly introduced ...

return on his or her investment with a small risk of incurring a loss; an oncologist prescribes radiotherapy to a cancer patient so as to destroy the tumor without causing damage to healthy organs; an airline manager constructs schedules that incur small salary costs and that ensure smooth operation even in the case of disruptions. All three decision makers (DMs) are in a similar situation—they...

In this paper, a multiobjective simulated annealing (MOSA) method is introduced and discussed with the multiobjective evolutionary algorithms (MOEAs). Though the simulated annealing is a very powerful search algorithm and has shown good results in various singleobjective optimization fields, it has been seldom used for the multiobjective optimization because it conventionally uses only one sear...

During the last years multiobjective optimization has attracted the interest of those involved in the optimization field. Real problems usually involve the simultaneous optimization of several objectives, a fact that makes multiobjective optimization a priority. In 2001 Harmony Search Algorithm (HSA), a new optimization technique was presented. This novel metaheuristic is based on the observati...

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) and an improved non-dominating sorting multiobjective genetic algorithm (NSGA-II) are two well known multiobjective evolutionary algorithms (MOEAs) in the field of evolutionary computation. This paper mainly reviews their hybrid versions and some other algorithms which are developed for solving multiobjective optimization pro...

The concept of optimization—finding the extrema of a function that maps candidate ‘solutions’ to scalar values of ‘quality’—is an extremely general and useful idea that can be, and is, applied to innumerable problems in science, industry, and commerce. However, the vast majority of ‘real’ optimization problems, whatever their origins, comprise more than one objective; that is to say, ‘quality’ ...

Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimiz...

Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA) is proposed. It is the first biogeography optimization algorithm for constrained multiobjective optimization. In CMBOA, a disturbance migration operator is designed to generate di...