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Fennec Fox Optimization: A New Nature-Inspired Optimization Algorithm
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DOI：10.1109/ACCESS.2022.3197745.png)
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Abstract
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This paper proposes a new nature-based metaheuristic algorithm called Fennec Fox Optimization (FFA), mimicking two natural behaviors of the animal Fennec Fox in nature. Concretely, Fennec's digging ability and escape strategy from wild predators were the fundamental inspiration for the proposed FFA. The mathematical model of FFA is presented in two phases based on imitating these two behaviors. First, the efficiency of FFA was evaluated in the optimization of sixty-eight standard benchmark functions and four engineering design problems. Second, FFA performance is challenged against eight well-known optimization algorithms. The optimization results show that FFA perfectly balances exploration and exploitation in searching for the global optimum. Hence, FFA can provide suitable solutions to optimization problems. The comparison of results indicates the superiority of FFA in most objective functions over competitor algorithms in providing the optimal solution.
Keywords:
Optimization
Behavioral sciences
Metaheuristics
Search problems
Benchmark testing
Mathematical models
Linear programming
Fennec fox
optimization
nature-inspired
optimization algorithm
metaheuristic
exploration
exploitation
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
Organization
Cited Papers
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