A Modified Artificial Algae Algorithm for Large Scale Global Optimization Problems

Abstract

Optimization technology is used to accelerate decision-making processes and to increase the quality of decision making inmanagement and engineering problems. The development technology has made real world problems large and complex. Many optimizationmethods that proposed for solving large-scale global optimization (LSGO) problems suffer from the “curse of dimensionality”, whichimplies that their performance deteriorates quickly as the dimensionality of the search space increases. Therefore, more efficient and robustalgorithms are needed. When literature on large-scale optimization problems is examined, it is seen that algorithms with effective globalsearch ability have better results. For the purpose, in this paper Modified Artificial Algae Algorithm (MAAA) is proposed by modifyingoriginal version of Artificial Algae Algorithm (AAA) inspiring by Differential Evolution Algorithm (DE)’s mutation strategies. AAA andMAAA are compared with each other by operating with the first 10 benchmark functions of CEC2010 Special Session on Large ScaleGlobal Optimization. The results show that hybridization process that applied by updating an additional fourth dimension with mutationstrategies of DE after the helical motion of the AAA algorithm, contributes exploration phase and improves the AAA performance onLSGO.