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MOCSA: a multi-objective crow search algorithm for multi-objective optimization

Nobahari, H ; Sharif University of Technology | 2017

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  1. Type of Document: Article
  2. DOI: 10.1109/CSIEC.2017.7940171
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2017
  4. Abstract:
  5. In this paper, an extension of the recently developed Crow Search Algorithm (CSA) to multi-objective optimization problems is presented. The proposed algorithm, called Multi-Objective Crow Search Algorithm (MOCSA), defines the fitness function using a set of determined weight vectors, employing the max-min strategy. In order to improve the efficiency of the search space, the performance space is regionalized using specific control points. A new chasing operator is also employed in order to improve the convergence process. Numerical results show that MOCSA is closely comparable to well-known multi-objective algorithms. © 2017 IEEE
  6. Keywords:
  7. Convergence process ; Multi-objective optimization ; Weight vectors ; Artificial intelligence ; Evolutionary algorithms ; Learning algorithms ; Optimization ; Swarm intelligence ; Crow search algorithm ; Fitness functions ; Max-min strategy ; Multi objective algorithm ; Multi-objective optimization problem ; Performance spaces ; Search algorithms ; Weight vector ; Multiobjective optimization
  8. Source: 2nd Conference on Swarm Intelligence and Evolutionary Computation, CSIEC 2017, 7 March 2017 through 9 March 2017 ; 2017 , Pages 60-65 ; 9781509043293 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/7940171