Abstract
In this article using Cuckoo Optimization Algorithm and simple additive weighting method the hybrid COAW algorithm is presented to solve multi-objective problems. Cuckoo algorithm is an efficient and structured method for solving nonlinear continuous problems. The created Pareto frontiers of the COAW proposed algorithm are exact and have good dispersion. This method has a high speed in finding the Pareto frontiers and identifies the beginning and end points of Pareto frontiers properly. In order to validation the proposed algorithm, several experimental problems were analyzed. The results of which indicate the proper effectiveness of COAW algorithm for solving multi-objective problems.
Highlights
There are many methods for solving nonlinear constrained programming problems such as Newton, Genetic algorithm, the algorithm of birds and so on
The first section introduces Cuckoo optimization algorithm, in the second section Simple Additive Weighting (SAW) method is discussed as a combined method for solving multiobjective described
After the implementation of the proposed approach on test problems the Pareto frontiers are obtained according to figures 3, 5 and 7 in order to compare the COAW method with other methods, ranking method, DEA method and GDEA method are implemented on problems
Summary
There are many methods for solving nonlinear constrained programming problems such as Newton, Genetic algorithm, the algorithm of birds and so on. In this paper using the emerging Cuckoo Optimization Algorithm and simple additive weighting a method to solve multi-objective problems is presented. Klein and Hannan for multiple objective integer linear programming problems (MOILP) presented and algorithm in which some additional restrictions is used to remove the known dominant solutions [2]. Deb analyzed the solution of multi-objective problems by evolutionary algorithms [5]. Reyesseerra and Coello Coello analyzed the solution of multi-objective problems by particle swarm [6]. Nebro et al analyzed a new method based on particle swarm algorithm for solving multiobjective optimization problems [9]. The first section introduces Cuckoo optimization algorithm, in the second section Simple Additive Weighting (SAW) method is discussed as a combined method for solving multiobjective described. The fourth section provides the proposed implemented approach, numerical results and a comparison which is made with other methods
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