Naturally occurring phenomenon serves as an unbiased guide for solving various optimization problems. This paper compiles some of the population-based, stochastic optimization algorithms including the recently developed social impact theory based optimizer, SITO. The current state of research, including the natural phenomena followed by each and some of their applications to solve various optim...

Due to the fact that the error surface of adaptive infinite impulse response (IIR) systems is generally nonlinear and multimodal, the conventional derivative based techniques fail when used in adaptive identification of such systems. In this case, global optimization techniques are required in order to avoid the local minima. Harmony search (HS), a musical inspired metaheuristic, is a recently ...

In this paper, a new artificial intelligence technique, Harmony Search (HS), will be used for the optimization of a classical order PID for a two-area load frequency control (LFC) model using the participation factor concept. The HS has four main variants, these variants had been used for the optimization of classical order PID controllers in case of centralized control scheme, the results had ...

Harmony Search (HS) is a meta-heuristic algorithm which bases its operation on the musical improvisation process. Recently, HS has become a popular algorithm in the evolutionary computation fielddue to its superiority to many other algorithms. As a consequence, in this paper, HS algorithm, its improvements and applications in many fields, such as operations research and computer science, are di...

This study deals with performance-based design optimization (PBDO) of steel moment frames employing four different metaheuristics consisting of genetic algorithm (GA), ant colony optimization (ACO), harmony search (HS), and particle swarm optimization (PSO). In order to evaluate the seismic capacity of the structures, nonlinear pushover analysis is conducted (PBDO). This method is an iterative ...

This paper presents a novel hybrid data clustering algorithm based on parameter adaptive harmony search algorithm. The recently developed parameter adaptive harmony search algorithm (PAHS) is used to refine the cluster centers, which are further used in initializing Expectation-Maximization clustering algorithm. The optimal number of clusters are determined through four well-known cluster valid...

This article presents a novel modification of the Harmony Search (HS) algorithm that is able to self-tune as the search progress. This adaptive behavior is independent of total iterations. Moreover, it requires less iterations and provides more precision than other variants of HS. Its effectiveness and performance was assessed, comparing our data against four well known and recent modifications...

Harmony search HS method is an emerging metaheuristic optimization algorithm. In this paper, an improved harmony search method based on differential mutation operator IHSDE is proposed to deal with the optimization problems. Since the population diversity plays an important role in the behavior of evolution algorithm, the aim of this paper is to calculate the expected population mean and varian...

Most engineering optimization algorithms are based on numerical linear and nonlinear programming methods that require substantial gradient information and usually seek to improve the solution in the neighborhood of a starting point. These algorithms, however, reveal a limited approach to complicated real-world optimization problems. If there is more than one local optimum in the problem, the re...

In this paper, the Harmony Search (HS)-aided BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can result in local optima in the training of BP neural networks, which may significantly affect their approximation performances. Three HS methods, the original version and two new v...