A Hybrid Meta-Heuristic Algorithm for High Performance Computing

نوع مقاله : علمی-پژوهشی

نویسندگان

Computer Engineering Department, Yazd University, Yazd, Iran.

چکیده

Regarding optimization problems, there is a high demand for high-performance algorithms that can process the problem solution-space efficiently and find the best ones quite quickly. An approach to get this target is based on using swarm intelligence algorithms; these algorithms apply a population of simple agents to communicate locally with one another and with their surroundings. In this paper, we propose a novel approach based on combining the characteristics of the two algorithms: Cat Swarm Optimization (CSO) and the Shuffled Frog Leaping Algorithm (SFLA). The experimental results show the convergence ratio of our hybrid SFLA-CSO algorithm is seven times higher than that of CSO and five times higher than the convergence ratio of the standard SFLA algorithm. The obtained results also revealed that the hybrid method speeds up the convergence significantly, and reduces the error rate. We compared the proposed hybrid algorithm against the famous relevant algorithms PSO, ACO, ABC, GA, and SA; the results are valuable and promising.

کلیدواژه‌ها

عنوان مقاله [English]

A Hybrid Meta-Heuristic Algorithm for High Performance Computing

نویسندگان [English]

  • E. Mahdipour
  • M. Ghasemzadeh
Computer Engineering Department, Yazd University, Yazd, Iran.
چکیده [English]

Regarding optimization problems, there is a high demand for high-performance algorithms that can process the problem solution-space efficiently and find the best ones quite quickly. An approach to get this target is based on using swarm intelligence algorithms; these algorithms apply a population of simple agents to communicate locally with one another and with their surroundings. In this paper, we propose a novel approach based on combining the characteristics of the two algorithms: Cat Swarm Optimization (CSO) and the Shuffled Frog Leaping Algorithm (SFLA). The experimental results show the convergence ratio of our hybrid SFLA-CSO algorithm is seven times higher than that of CSO and five times higher than the convergence ratio of the standard SFLA algorithm. The obtained results also revealed that the hybrid method speeds up the convergence significantly, and reduces the error rate. We compared the proposed hybrid algorithm against the famous relevant algorithms PSO, ACO, ABC, GA, and SA; the results are valuable and promising.

کلیدواژه‌ها [English]

  • Cat swarm optimization
  • Convergence rate
  • Shuffled frog leaping algorithm
  • Swarm intelligence

مراجع

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مجله مهندسی برق دانشگاه تبریز
دوره 51، شماره 1 - شماره پیاپی 95
اردیبهشت 1400
صفحه 97-107

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