Hybrid harmony search algorithm for continuous optimization problems

Harmony Search (HS) algorithm has been extensively adopted in the literature to address optimization problems in many different fields, such as industrial design, civil engineering, electrical and mechanical engineering problems. In order to ensure its search performance, HS requires extensive tuning of its four parameters control namely harmony memory size (HMS), harmony memory consideration rate (HMCR), pitch adjustment rate (PAR), and bandwidth (BW). However, tuning process is often cumbersome and is problem dependent. Furthermore, there is no one size fits all problems. Additionally, despite many useful works, HS and its variant still suffer from weak exploitation which can lead to poor convergence problem. Addressing these aforementioned issues, this thesis proposes to augment HS with adaptive tuning using Grey Wolf Optimizer (GWO). Meanwhile, to enhance its exploitation, this thesis also proposes to adopt a new variant of the opposition-based learning technique (OBL). Taken together, the proposed hybrid algorithm, called IHS-GWO, aims to address continuous optimization problems. The IHS-GWO is evaluated using two standard benchmarking sets and two real-world optimization problems. The first benchmarking set consists of 24 classical benchmark unimodal and multimodal functions whilst the second benchmark set contains 30 state-of-the-art benchmark functions from the Congress on Evolutionary Computation (CEC). The two real-world optimization problems involved the three-bar truss and spring design. Statistical analysis using Wilcoxon rank-sum and Friedman of IHS-GWO’s results with recent HS variants and other metaheuristic demonstrate superior performance

A simplified binary harmony search algorithm for large scale 0-1 knapsack problems

As an important subset of combinatorial optimization, 0-1 knapsack problems, especially the high-dimensional ones, are often difficult to solve. This study aims to provide a new simplified binary harmony search (SBHS) algorithm to tackle such NP-hard problems arising in diverse research fields. The key difference between SBHS and other HS methods is in the process of improvisation. The differences among harmonies stored in harmony memory rather than the pitch adjustment rate (PAR) and step bandwidth (bw) are employed to produce new solutions and this can greatly alleviate the burden of setting these important factors manually. Moreover, the harmony memory considering rate (HMCR) is dynamically adjusted in terms of the dimension size to improve convergence of the algorithm. Therefore, the proposed method does not require any tedious process of proper parameter setting. To further enhance the population diversity, a specific heuristic based local search around infeasible solutions is carried out to obtain better quality solutions. A set of 10 low dimensional knapsack problems as well as large scale instances with up to 10,000 items are used to test the effectiveness of the proposed algorithm. Extensive comparisons are made with the most well-known state-of-the-art HS methods including 9 continuous versions and 5 binary-coded variants. The results reveal that the proposed algorithm can obtain better solutions in almost all cases and outperforms the other considered HS methods with statistical significance, especially for the large scale problems