A Modified Artificial Algae Algorithm For Large Scale Global Optimization Problems

Havva Gul Kocer, Sait Ali Uymaz
  • Havva Gul Kocer
    Selcuk University, Turkey

Abstract

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

Keywords

Artificial algae algorithm; CEC2010 benchmark; large scale global optimization

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Submitted: 2018-10-21 10:25:00
Published: 2018-12-27 18:57:41
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