This paper aims to present a new multi-community meta-heuristic optimization algorithm, which is called shuffled shepherd optimization algorithm (SSOA). In this algorithm.
The agents are first separated into multi-communities and the optimization process is then performed mimicking the behavior of a shepherd in nature operating on each community.
A new multi-community meta-heuristic optimization algorithm called a shuffled shepherd optimization algorithm is developed in this paper and applied to some attractive examples.
A new metaheuristic is presented and tested with some classic benchmark problems and some attractive structures are optimized.
Compliance with ethical standards: Conflict of interest: No potential conflict of interest was reported by the authors.
Kaveh, A. and Zaerreza, A. (2020), "Shuffled shepherd optimization method: a new Meta-heuristic algorithm", Engineering Computations, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/EC-10-2019-0481Download as .RIS
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