We develop a hybrid algorithm using Genetic Algorithms (GA) and Simulated Annealing (SA) to solve multi-objective step function maximization problems. We then apply the algorithm to a specific economic problem which is taken out of the corporate governance literature.

Non-hierarchical cluster analysis for panel data is known to be hampered by structural preservation, computational complexity and efficiency, and dependency problems. Resolving these issues becomes increasingly important as efficient collection and maintenance of panel data make application more...

The fuzzy C-means (FCM) algorithm is a commonly used fuzzy clustering method which conducts data clustering by randomly selecting initial centroids. With larger data size or attribute dimensions, clustering results may be affected and more repetitive computations are required. To compensate the...

Well established conventional algorithms are available for solving the optimum power flow (OPF) problem. But the recent trend is to use the tools such as genetic algorithms (GAs) evolutionary programming technique, etc., because of some of their superior qualities. Simulated annealing (SA) is...

Hybridisation of algorithms to develop efficient solution methods for solving various NP-hard problems has been a practice for the last few years. It can be seen that researchers have tried different permutations and combinations of various exact, heuristic and metaheuristic algorithms to...