Abstract

This paper describes a new variant of harmony search algorithm which is inspired by a well‐known item “elite decision making.” In the new algorithm, the good information captured in the current global best and the second best solutions can be well utilized to generate new solutions, following some probability rule. The generated new solution vector replaces the worst solution in the solution set, only if its fitness is better than that of the worst solution. The generating and updating steps and repeated until the near‐optimal solution vector is obtained. Extensive computational comparisons are carried out by employing various standard benchmark optimization problems, including continuous design variables and integer variables minimization problems from the literature. The computational results show that the proposed new algorithm is competitive in finding solutions with the state‐of‐the‐art harmony search variants.

Highlights

  • In 2001, Geem et al 1 proposed a new metaheuristic algorithm, harmony search HS algorithm, which imitates the behaviors of music improvisation process

  • These features increase the flexibility of the HS algorithm and have led to its application to optimization problems in different areas including music composition 2, Sudoku puzzle solving 3, structural design 4, 5, ecological conservation 6, Journal of Applied Mathematics and aquifer parameter identification 7

  • Numerical experiments based on benchmark problems showed that the proposed SGHS algorithm was more effective in finding better solutions than the existing HS, HIS, and Global Best Harmony Search (GHS) algorithms

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Summary

Introduction

In 2001, Geem et al 1 proposed a new metaheuristic algorithm, harmony search HS algorithm, which imitates the behaviors of music improvisation process. Instead of a gradient search, the HS algorithm uses a stochastic random search that is based on the harmony memory considering rate and the pitch adjusting rate so that derivative information is unnecessary. These features increase the flexibility of the HS algorithm and have led to its application to optimization problems in different areas including music composition 2 , Sudoku puzzle solving 3 , structural design 4, 5 , ecological conservation 6 , Journal of Applied Mathematics and aquifer parameter identification 7. Pan et al used the good information captured in the current global best solution to generate new harmonies.

Harmony Search Algorithm
The Improved HS Algorithm
EDMHS Algorithm for Continuous Design Variables Problems
EDMHS Algorithm for Integer Variables Problems
Numerical Examples
Rosenbrock Function Consider the following:
Eason and Fenton’s Gear Train Inertia Function
Wood Function Consider the following:
Powell Quartic Function Consider the following:
Griewank function
Integer Variables Examples
Test Problem 5
Conclusion
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