Abstract
Harmony search (HS) algorithm is an emerging population-based metaheuristic algorithm, which is inspired by the music improvisation process. The HS method has been developed rapidly and applied widely during the past decade. In this paper, an improved global harmony search algorithm, named harmony search based on teaching-learning (HSTL), is presented for high dimension complex optimization problems. In HSTL algorithm, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to maintain the proper balance between convergence and population diversity, and dynamic strategy is adopted to change the parameters. The proposed HSTL algorithm is investigated and compared with three other state-of-the-art HS optimization algorithms. Furthermore, to demonstrate the robustness and convergence, the success rate and convergence analysis is also studied. The experimental results of 31 complex benchmark functions demonstrate that the HSTL method has strong convergence and robustness and has better balance capacity of space exploration and local exploitation on high dimension complex optimization problems.
Highlights
With the development of scientific technology, many reallife optimization problems are becoming more and more complex and difficult
In order to achieve the most satisfactory optimization performance by applying the HS algorithm to a given problem, we develop a novel harmony search algorithm combined with teaching-learning strategy, in which both new harmony generation strategies and associated control parameter values can be dynamically changed according to the process of evolution
The considered functions are as folows. (i) Nonseparable functions: (a) F15: shifted Rosenbrock’s function, (b) F17: shifted Griewank’s function, (c) NS-F21: nonshifted Extended F10, (d) NS-F22: nonshifted Bohachevsky. (ii) Other component functions: (a) F13: shifted sphere function, (b) F16: shifted Rastrigin function, (c) F20: Schwefel2.22 function
Summary
With the development of scientific technology, many reallife optimization problems are becoming more and more complex and difficult. Most popular swarm intelligence optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) algorithm, have been successfully applied to large-scale complicated problems of scientific and engineering computing. The aforementioned applications show that HS algorithm has significant in solving complex engineering application problems. It has strong ability of exploration and has a cheap running cost. The classical harmony search algorithm is not efficient enough for solving large-scale problems, which has a slow convergence speed and low-precision solution. Mahdavi et al proposed an improved HS algorithm (IHS) [8] that employed a novel method generating new solution vectors which enhanced accuracy and convergence speed. The new variant named GHS (Global Best Harmony Search) [10] reportedly outperformed the HS and IHS algorithm over the benchmark problems
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