The book explores significant developments in the harmony search algorithm, highlighting its theoretical foundations and practical applications across various fields. Key topics include its use in robotics, visual tracking, web text data mining, power flow planning, fuzzy control systems, irrigation, and logistics, demonstrating the algorithm's versatility and effectiveness in solving complex problems.
Traditional mathematical optimization methods, such as linear programming (LP), nonlinear programming (NLP) and dynamic programming (DP), often have drawbacks in practice. To overcome these drawbacks, various meta-heuristic optimization techniques have been developed. This study proposes a new meta- heuristic algorithm which mimics musical performance, and names as Harmony Search (HS). The HS algorithm is applied to five problems: a continuous function optimization, a hydrologic parameter calibration, a traveling salesman problem, an optimal design of water supply network, and an optimal expansion of water supply network. The comparison of solutions obtained by HS and other optimization techniques shows that the HS algorithm can find better solutions in less number of iterations. The new algorithm can solve continuous-variable problems as well as discrete- variable problems without unnecessary restrictions imposed by calculus-based algorithms.
Theory and Applications
Calculus has been used in solving many scientific and engineering problems. For optimization problems, however, the differential calculus technique sometimes has a drawback when the objective function is step-wise, discontinuous, or multi-modal, or when decision variables are discrete rather than continuous. Thus, researchers have recently turned their interests into metaheuristic algorithms that have been inspired by natural phenomena such as evolution, animal behavior, or metallic annealing. This book especially focuses on a music-inspired metaheuristic algorithm, harmony search. Interestingly, there exists an analogy between music and each musical instrument corresponds to each decision variable; musical note corresponds to variable value; and harmony corresponds to solution vector. Just like musicians in Jazz improvisation play notes randomly or based on experiences in order to find fantastic harmony, variables in the harmony search algorithm have random values or previously-memorized good values in order to find optimal solution.