Abstract
This paper presents a study of two popular metaheuristics, namely differential evolution (DE) and harmony search (HS), including a proposal for the dynamic modification of parameters of each algorithm. The methods are applied to two cases, finding the optimal design of a fuzzy logic system (FLS) applied to the optimal design of a fuzzy controller and to the optimization of mathematical functions. A fuzzy logic controller (FLC) of the Takagi–Sugeno type is used to find the optimal design in the membership functions (MFs) for the stabilization problem of an autonomous mobile robot following a trajectory. A comparative study of the results for two modified metaheuristic algorithms is presented through analysis of results and statistical tests. Results show that, statistically speaking, optimal fuzzy harmony search (OFHS) is better in comparison to optimal fuzzy differential evaluation (OFDE) for the two presented study cases.
Highlights
The harmony search algorithm (HS) has proven to be an interesting method in solving complex problems in intelligent computing, and several authors are focused on implementing this algorithm in different fields on computer science
Liu et al in [6], an improved HS is described by Ouyang et al in [7], an HS with dynamic adaptation of Parameters for problem control presented by Peraza et al in [8], an HS for feature selection for facial emotion recognition using cosine similarity by Saha et al in [9], a novel HS and its application to data clustering presented by Talaei et al in [10], a case study to test a fuzzy HS is described by Valdez et al in [11], and an improved differential-based HS with linear dynamic domain is presented by Zhu et al in [12]
Sci. 2020, 10, 6146 algorithm, and some important works using differential evolution (DE) include an Adaptive DE with novel mutation strategies in multiple sub-populations presented by Cui et al in [13], a novel DE for solving constrained engineering optimization problems presented by Mohamed in [14], a hybrid real-code population-based incremental learning and DE for many-objective optimization of an automotive floor-frame described by Pholdee et al in [15], time series forecasting for building energy consumption using weighted support vector regression with DE optimization technique presented by Zhang et al in [16], an improved adaptive DE for continuous optimization outlined by Yi et al in [17], and an adaptive DE with sorting crossover rate for continuous optimization problems presented by Zhou et al in [18]
Summary
The harmony search algorithm (HS) has proven to be an interesting method in solving complex problems in intelligent computing, and several authors are focused on implementing this algorithm in different fields on computer science. Liu et al in [6], an improved HS is described by Ouyang et al in [7], an HS with dynamic adaptation of Parameters for problem control presented by Peraza et al in [8], an HS for feature selection for facial emotion recognition using cosine similarity by Saha et al in [9], a novel HS and its application to data clustering presented by Talaei et al in [10], a case study to test a fuzzy HS is described by Valdez et al in [11], and an improved differential-based HS with linear dynamic domain is presented by Zhu et al in [12] Another important meta-heuristic that has proven to be an excellent algorithm based on its implementation results in different areas of intelligent computing is the differential evolution (DE).
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