A modified colonial competitive algorithm for optimizing convex functions

Abstract

Purpose -This article is going to introduce a modified variant of the imperialist competitive algorithm (ICA). The paper aims to discuss these issues. Design/methodology/approach -ICA is a meta-heuristic algorithm that is introduced based on a socio-politically motivated global search strategy. It is a population-based stochastic algorithm to control more countries. The most powerful countries are imperialists and the weakest countries are colonies. Colonies movement toward their relevant imperialist, and making a competition among all empires to posses the weakest colonies of the weakest empires, form the basis of the ICA. This fact that the imperialists also need to model and they move towards top imperialist state is the most common type of political rules from around the world. This paper exploits these new ideas. The modification is the empire movement toward the superior empire for balancing the exploration and exploitation abilities of the ICA. Findings -The algorithms are used for optimization that have shortcoming to deal with accuracy rate and local optimum trap and they need complex tuning procedures. MICA is proposed a way for optimizing convex function with high accuracy and avoiding to trap in local optima rather than using original ICA algorithm by implementing some modification on it. Originality/value -Therefore, several solution procedures, including ICA, modified ICA, and genetic algorithm and particle swarm optimization algorithm are proposed. Finally, numerical experiments are carried out to evaluate the effectiveness of models as well as solution procedures. Test results present the suitability of the proposed modified ICA for convex functions with little fluctuations.

Evaluation of employee profiles using a hybrid clustering and optimization model

“…4.1.1 Optimization. In the simplest case, an optimization problem consists of maximizing the enterprise profit and fully utilizing the limited production resources or minimizing the enterprise cost as a real function Esmaeilzadeh (2013). Many global optimizing algorithms such as SA by Kirkpatrick (Kirkpatrick et al, 1983), GAs by Holland (1975), ant colony optimization by Dorigo (1992), TS by Glover (1986), and particle swarm optimization by Kennedy and Eberhart (1995) are preferable.…”

“…4.1.3 Fuzzy imperialistic competitive algorithm (FICA). EAs are population-based meta-heuristic optimization algorithms that use biology-inspired mechanisms like mutation, crossover, natural selection and survival of the fittest in order to refine a set of solution candidates iteratively (Esmaeilzadeh, 2013). ICA is an EA that has been introduced by Atashpaz Gargari and Lucas (2007).…”