Xiaolei Wang

A hybrid optimization method of harmony search and opposition-based learning

The harmony search (HS) method is an emerging meta-heuristic optimization algorithm. However, like most of the evolutionary computation techniques, it sometimes suffers from a rather slow search speed, and fails to find the global optimum in an efficient way. In this article, a hybrid optimization approach is proposed and studied, in which the HS is merged together with the opposition-based learning (OBL). The modified HS, namely HS-OBL, has an improved convergence property. Optimization of 24 typical benchmark functions and an optimal wind generator design case study demonstrate that the HS-OBL can indeed yield a superior optimization performance over the regular HS method.

A hybrid PBIL-based harmony search method

The harmony search (HS) method is a popular meta-heuristic optimization algorithm, which has been extensively employed to handle various engineering problems. However, it sometimes fails to offer a satisfactory convergence performance under certain circumstances. In this paper, we propose and study a hybrid HS approach, HS–PBIL, by merging the HS together with the population-based incremental learning (PBIL). Numerical simulations demonstrate that our HS–PBIL is well capable of outperforming the regular HS method in dealing with nonlinear function optimization and a practical wind generator optimization problem.

Harmony search method: theory and applications

The Harmony Search (HS) method is an emerging metaheuristic optimization algorithm, which has been employed to cope with numerous challenging tasks during the past decade. In this paper, the essential theory and applications of the HS algorithm are first described and reviewed. Several typical variants of the original HS are next briefly explained. As an example of case study, a modified HS method inspired by the idea of Pareto-dominance-based ranking is also presented. It is further applied to handle a practical wind generator optimal design problem.

A New Harmony Search method in optimal wind generator design

The Harmony Search (HS) method is an emerging meta-heuristic optimization algorithm. However, like most of the evolutionary computation techniques, it does not store or utilize the useful knowledge gained during its search procedure in an efficient way. In this paper, we propose and study a new optimization approach, in which the HS method is merged together with the Cultural Algorithm (CA). Our modified HS method, namely HS-CA, has the feature of embedded problem-solving knowledge. The HS-CA is further employed in an optimal wind generator design problem, and it can yield a superior optimization performance over the original HS method.

A Hybrid Harmony Search Method Based on OBL

The Harmony Search (HS) method is an emerging meta-heuristic optimization algorithm. However, like most of the evolutionary computation techniques, it sometimes suffers from a rather slow search speed, and fails to find the global optima in an efficient way. In this paper, we propose and study a hybrid optimization approach, in which the HS is merged together with the Opposition-Based Learning (OBL). Our modified HS, namely HS-OBL, has an improved convergence property. Simulations of 23 typical benchmark problems demonstrate that the HS-OBL can indeed yield a superior optimization performance over the regular HS method.

Motor fault diagnosis using negative selection algorithm

Abstract In this paper, we propose a novel multi-level negative selection algorithm (NSA)-based motor fault diagnosis scheme. The hierarchical fault diagnosis approach takes advantage of the feature signals of the healthy motors so as to generate the NSA detectors and further uses the analysis of the activated detectors for fault diagnosis. It can not only efficiently detect incipient motor faults, but also correctly identify the corresponding fault types. The applicability of our motor fault diagnosis method is examined using two real-world problems in computer simulations.

The Overview of Harmony Search

When musicians compose the harmony, they usually try various possible combinations of the music pitches stored in their memory, which can be considered as a optimization process of adjusting the input (pitches) to obtain the optimal output (perfect harmony). Harmony search draws the inspiration from harmony improvisation, and has gained considerable results in the field of optimization, although it is a relatively NIC algorithm. With mimicking the rules of various combining pitches, harmony search has two distinguishing operators different from other NIC algorithms: harmony memory considering rate (HMCR) and pitch adjusting rate (PAR) that are used to generate and further mutate a solution, respectively. This candidate generation mechanism and single search memory involved decide its excellence in structure simplicity and small initial population. This chapter presents the discussions of the inspiration of harmony search, the basic harmony search optimization algorithm, and an overview of different application areas of the harmony search.

A memetic-inspired harmony search method in optimal wind generator design

The harmony search (HS) method is an emerging meta-heuristic optimization algorithm inspired by the natural musical performance process, which has been extensively applied to handle numerous optimization problems during the past decade. However, it usually lacks of an efficient local search capability, and may sometimes suffer from weak convergence. In this paper, a memetic HS method, m-HS, with local search function is proposed and studied. The local search in the m-HS is based on the principle of bee foraging like strategy, and performs only at selected harmony memory members, which can significantly improve the efficiency of the overall search procedure. Compared with the original HS method and particle swarm optimization (PSO), our m-HS has been demonstrated in numerical simulations of 16 typical benchmark functions to yield a superior optimization performance. The m-HS is further successfully employed in the optimal design of a practical wind generator.

A novel Harmony Search method with dual memory

The Harmony Search (HS) method is an emerging meta-heuristic optimization algorithm, which has been widely employed to deal with various optimization problems during the past decade. However, like most of the evolutionary computation techniques, it sometimes suffers from a rather slow search speed, and even fails to find the global optima in an efficient way. In this paper, a new HS method with dual memory, namely DUAL-HS, is proposed and studied. The secondary memory in the DUAL-HS takes advantage of the Opposition-Based Learning (OBL) to evolve so that the quality of all the harmony memory members can be significantly improved. Optimization of 25 typical benchmark functions demonstrate that compared with the regular HS method, our DUAL-HS has an enhanced convergence property.

A Harmony Search-Based Differential Evolution Method

The Differential Evolution (DE) and Harmony Search (HS) are two well-known nature-inspired computing techniques. Both of them can be applied to effectively cope with nonlinear optimization problems. In this paper, we propose and study a new DE method, DE-HS, by utilizing the fresh individual generation mechanism of the HS. The HS-based approach can enhance the local search capability of the original DE method. Optimization of several benchmark functions and a real-world wind generator demonstrate that our DE-HS has an improved convergence property.