Normative Improved Artificial Fish Swarm Algorithm (NIAFSA) for Global Optimization
Weng-Hooi Tan1, JunitaMohamad-Saleh2
1Weng-Hooi Tan, School of Electrical & Electronic Engineering, Engineering Campus, University Sains, Malaysia.
2Junita Mohamad-Saleh, School of Electrical & Electronic Engineering, Engineering Campus, University Sains Malaysia.
Manuscript received on 10 December 2018 | Revised Manuscript received on 17 December 2018 | Manuscript Published on 26 December 2018 | PP: 480-484 | Volume-8 Issue- 2S2 December 2018 | Retrieval Number: ES2142017519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Optimization is an important field of research. Various optimization algorithms have been developed to solve optimization problems. Nevertheless, many have not succeeded to achieve the real global optima. Hence, a research on designing and developing a global search and optimization algorithm is presented in this paper. The aim is to enhance the performance of global and local searching strategy in term of best optimal solution. The fish swarm algorithm with the particle swarm optimization with extended memory (PSOEM-FSA) is hybridized with the normative knowledge to become a normative improved fish swarm algorithm (NIAFSA). The feature of global crossover breeding is installed into the proposed algorithm to achieve relatively consistent results. A random initialization of initial population is introduced to spread out the candidates of artificial fishes (AFs) over the solution space. In addition, parameters such as visual and step are made adaptive along the iteration process to balance the contradiction between global and local search ability. The collected results are analyzed and compared with few existing fish swarm variant algorithms to verify the performance of the proposed algorithm.
Keywords: Artificial Fish Swarm Algorithm (AFSA), Adaptive Visual and Step, Particle Swarm Optimization (PSO), PSO with Extended Memory (PSOEM), Normative Knowledge, Global Crossover.
Scope of the Article: Computer Architecture and VLSI