A Novel Hybrid Bat Crow Search Algorithm For Solving Optimization Problems

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Danacı M. , Akhdır Z.

EUROASIA JOURNAL OF MATHEMATICS-ENGINEERING NATURAL & MEDICAL SCIENCES, vol.2, no.7, pp.40-45, 2019 (Refereed Journals of Other Institutions)

  • Publication Type: Article / Article
  • Volume: 2 Issue: 7
  • Publication Date: 2019
  • Page Numbers: pp.40-45


Meta heuristic algorithms like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and other

algorithms are great and famous techniques used to solve many hard and complex optimization

problems. This paper presents a new hybrid algorithm named Hybrid Bat Crow Search Algorithm

(HBCSA). To achieve this algorithm, two algorithms were considered. The algorithms are Crow

Search Algorithm (CSA) and Bat Algorithm (BA). The advantageous points of the two algorithms

were taken into consideration and used to design an effective hybrid algorithm that can give

significantly high performance in many benchmark functions. In addition, quantum behaved PSO

equation is used in this hybrid algorithm. This leaded to better results when testing the algorithm

against Benchmark problems. The combination of concept and functionality of Bat and Crow

algorithms enable the suggested hybrid algorithm of making an appropriate trade-off between

exploration and exploitation capabilities of the new algorithm.

For the purpose of evaluating the performance of the new Hybrid Bat Crow Search Algorithm

(HBCSA), some well known Benchmark functions were utilized. In the new algorithm every member

in the swarm will have behave like a crow in the sense of observing other members in the swarm to

see where they hide their foods. In the same time, as in bats, every member will use echo system while

searching its own solution. Echo system is integrated with PSO equations. Each member has an

awareness parameter as in CSA. According to awareness parameter a member can know whether if

another member is following it or no. These are the basic lines of the new HBCSA. The results

indicated that the proposed HBCSA can produce very competitive solution when compared to other

famous and state of the art meta-heuristic algorithms.

Key Words: Meta-Heuristic, Crow Search Algorithm, Bat Algorithm, Benchmark Functions.