An Experimental Study of a Fuzzy Adaptive Emperor Penguin Optimizer for Global Optimization Problem

An Experimental Study of a Fuzzy Adaptive Emperor Penguin Optimizer for Global Optimization Problem

Blog Article

Emperor Penguin Optimizer (EPO) is a recently developed population-based meta-heuristic algorithm that simulates the huddling behavior of emperor penguins.Mixed results have been observed on the performance of EPO in multiple-rings solving general optimization problems.Within the EPO, two parameters need to be tuned (namely ${f}$ and ${l}$ ) to ensure a good balance between exploration (i.

e., roaming unknown locations) and exploitation (i.e.

, manipulating the current known best).Since the search contour varies depending on the optimization problem, the tuning of $f$ and $l$ is problem-dependent, and there is no one-size-fits-all approach.To alleviate these problems, an adaptive mechanism can be introduced in EPO.

This paper proposes a fuzzy adaptive variant of EPO, namely Fuzzy Adaptive Emperor Penguin Optimizer (FAEPO), to solve this problem.As the name suggests, FAEPO can adaptively tune the parameters $f$ and $l$ RECHARGE throughout the search based on three measures (i.e.

, quality, success rate, and diversity of the current search) via fuzzy decisions.A test suite of twelve optimization benchmark test functions and three global optimization problems (Team Formation Optimization - TFO, Low Autocorrelation Binary Sequence - LABS, and Modified Condition/Decision Coverage - MC/DC test case generation) were solved using the proposed algorithm.The respective solution results of the benchmark meta-heuristic algorithms were compared.

The experimental results demonstrate that FAEPO significantly improved the performance of its predecessor (EPO) and gives superior performance against the competing meta-heuristic algorithms, including an improved variant of EPO (IEPO).