Abstract
Exploration and exploitation are two essential components for any optimization algorithm. Much exploration leads to oscillation and premature convergence while too much exploitation slows down the optimization algorithm and the optimizer may be stuck in local minima. Therefore, balancing the rates of exploration and exploitation at the optimization lifetime is a challenge. This study evaluates the impact of using chaos-based control of exploration/exploitation rates against using the systematic native control. Three modern algorithms were used in the study namely grey wolf optimizer (GWO), antlion optimizer (ALO) and moth-flame optimizer (MFO) in the domain of machine learning for feature selection. Results on a set of standard machine learning data using a set of assessment indicators prove advance in optimization algorithm performance when using variational repeated periods of declined exploration rates over using systematically decreased exploration rates.
Highlights
Chaos is the form of non-linear movement of the dynamics system and can experience any state according to its own regularity
Our aim is to propose an optimization algorithm based on two chaotic functions that employed to analyze their performance and impact on three bio-inspired optimization algorithms namely grey wolf optimization (GWO), antlion optimization (ALO), and moth-flame optimization (MFO)
This subsection provides a brief summary of the three modern bio-inspired optimizers namely grey wolf optimization (GWO), antlion optimization (ALO), and moth-flame optimization (MFO)
Summary
Chaos is the form of non-linear movement of the dynamics system and can experience any state according to its own regularity. Chaos has the ability of information processing that is applied widely in many fields like optimization problems, machine learning, and pattern recognition. The searching techniques based on chaos are similar to genetic algorithms (GA), but it uses the chaos variables. Because of the chaos is not repetition system, it can execute the overall search process in less computational time than stochastic ergodic techniques based on the probability [1]. The randomness role can be worked by chaotic motion rather than random processes in the optimization algorithms [2]. Chaos optimization algorithm (COA) has the chaos properties like ergodicity that can more efficiently escape from local minima than whatever other stochastic optimization algorithm [2]. COA utilizes the consistency exist as a part of a chaotic motion to escape from
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