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An Improved Harmony Search Algorithm with Differential Mutation Operator

Chakraborty P., Roy G.G., Das S., Jain D., Abraham A.

Fundamenta Informaticae

2009

Vol. 95, nr 4

401-426

Harmony Search (HS) is a recently developed stochastic algorithm which imitates the music improvisation process. In this process, the musicians improvise their instrument pitches searching for the perfect state of harmony. Practical experiences, however, suggest that the algorithm suffers from the problems of slow and/or premature convergence over multimodal and rough fitness landscapes. This paper presents an attempt to improve the search performance of HS by hybridizing it with Differential Evolution (DE) algorithm. The performance of the resulting hybrid algorithm has been compared with classical HS, the global best HS, and a very popular variant of DE over a test-suite of six well known benchmark functions and one interesting practical optimization problem. The comparison is based on the following performance indices - (i) accuracy of final result, (ii) computational speed, and (iii) frequency of hitting the optima.

Differential Evolution based Fuzzy Logic Controller for Nonlinear Process Control

Sastry K.K.N., Behera L., Nagrath I.J.

Fundamenta Informaticae

1999

Vol. 37, Nr 1,2

121-136

This paper presents an unconventional approach to adaptive fuzzy logic controller (FLC) de-sign wherein a new evolution strategy. Differential Evolution (DE) is used in the simultane-ous design of membership functions and rule sets for fuzzy logic controllers. Differential Evo-lution is an exceptionally simple, fast and robust population based search algorithm that is able to locate near-optimal solutions to difficult problems. This technique, which is similar to genetic algorithms, has been applied to the control of pH, which is a requirement in many chemical industries. Control of pH poses a difficult problem because of inherent nonlinearities and frequently changing process dynamics. This technique has been successfully implemented on a laboratory scale pH plant setup. The results have been compared with a simple GA based adaptive FLC where we have incorporated a search space smoothing function for achieving faster convergence and for ascertaining a global optimum. Results indicate that FLC's aug-mented with DE's offer a powerful alternative to GA based FLC's. Results also show that the search space smoothing function helps in faster convergence of a GA.