Abstract
In many optimization problems, the main goal is to improve a single performance index in which a minimum or maximum value of this index fully reflects the quality of the response obtained from a system. However, in some cases, it is impossible to rely solely on a single index, so a multi-objective optimization problem with multiple performance indicators is considered where the values of all of them should be optimized simultaneously. The mentioned process requires a multi-objective optimization algorithm that can deal with the complexity of problems with simultaneous indexes. This paper presents the multi-objective version of a recently proposed metaheuristic algorithm called Crystal Structure Algorithm (CryStAl) which was inspired by the principles underlying the formation crystal structures. For the performance evaluation of this algorithm which is called MOCryStAl, the benchmark problems of the Completions on Evolutionary Computation (CEC) on multi-objective optimization, called CEC-09, are utilized. Some real-world engineering design problems are used to evaluate the efficiency of the proposed approach. The results demonstrate that the proposed methods can provide excellent results in dealing with the considered multi-objective problems.
Highlights
Optimization is the art of finding the best answer among a set of possible solutions under some predefined conditions
WORK This paper presented the multi-objective version of the Crystal Structure Algorithm (CryStAl) as a recently proposed metaheuristic algorithm inspired by some geometric principles of crystal structures including the lattice and basis in the configuration of crystals
Some real-world engineering design problems were used to evaluate the efficiency of the proposed MOCryStAl approach
Summary
Optimization is the art of finding the best answer among a set of possible solutions under some predefined conditions. It is used for decision-making in various areas such as engineering, management, economics, and finance [1]–[4]. The technical specifications and limitations of system components as well as existing uncertainties should be determined and considered when defining the sought-after goals. These goals often require multidisciplinary optimization and modeling
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