# A New Fuzzy Harmony Search Algorithm Using Fuzzy Logic for Dynamic Parameter Adaptation
###### Abstract  
In this paper, a new fuzzy harmony search algorithm (FHS) for solving optimization problems is presented. FHS is based on a recent method using fuzzy logic for dynamic adaptation of the harmony memory accepting (_HMR_) and pitch adjustment (_PArate_) parameters that improve the convergence rate of traditional harmony search algorithm (HS). The objective of the method is to dynamically adjust the parameters in the range from 0.7 to 1. The impact of using fixed parameters in the harmony search algorithm is discussed and a strategy for efficiently tuning these parameters using fuzzy logic is presented. The FHS algorithm was successfully applied to different benchmarking optimization problems. The results of simulation and comparison studies demonstrate the effectiveness and efficiency of the proposed approach.  
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contribution in this paper is that currently there are methods and variants on the harmony search algorithm that use techniques to move its parameters, such as linear functions, statistical methods and other methods that help improve the convergence of the algorithm among others, but there is no method in the literature which adjusts the parameters using fuzzy logic. This is therefore the main scientific contribution, which is based on the study of parameters and performing experiments by trial and error, and in this way finding the best ranges for the outputs of the membership functions and fuzzy rules.  
In addition, a set of experiments was carefully performed to reveal the impact of control parameters when applied to benchmark mathematical functions, and a comparison with another method called the fuzzy bat algorithm [6] is presented.  
Similarly, there are papers on harmony search algorithm (HS) applications that use this algorithm to solve real world problems. To mention a few: self-adaptive harmony search algorithm for optimization [7] and a tabu harmony search-based approach to fuzzy linear regression [8], two improved harmony search algorithms for solving engineering optimization problems [9], combined heat and power economic dispatch by harmony search algorithm [5], a novel hybrid bat algorithm with harmony search for global numerical optimization [10], a hybrid Taguchi-harmony search algorithm for solving engineering optimization problems [11], and a dynamic multi-swarm particle swarm optimizer with harmony search [12].  
There are also papers of different metaheuristic algorithms that could be considered as related work, including the firefly algorithm [13; 14], the bat-inspired algorithm [15], differential evolution [16], virtual bee algorithms [17], hybridization of Particle swarm optimization (PSO) and Genetic Algorithms (GA) [18], fuzzy Particle swarm algorithm--a generalization of particle swarm optimization [19], and design of PSO-based fuzzy classification system [20].  
The rest of the paper is organized as follows: Section 2 shows the methodology for parameter adaptation and describes the proposed methods. Section 3 shows the experimentation with the fuzzy system and benchmark mathematical functions. Section 4 presents simulation results and a comparison of the harmony search algorithm with fuzzy harmony search algorithm. Section 5 presents simulation results and a comparison of the fuzzy harmony search algorithm with the fuzzy bat algorithm. Section 6 offers a statistical comparison. Finally, Section 7 describes the conclusions.