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The purpose of this paper is to introduce an evaluation methodology for employee profiles that will provide feedback to the training decision makers. Employee profiles play a crucial role in the evaluation process to improve the training process performance. This paper focuses on the clustering of the employees based on their profiles into specific categories that represent the employees’ characteristics. The employees are classified into following categories: necessary training, required training, and no training. The work may answer the question of how to spend the budget of training for the employees. This investigation presents the use of fuzzy optimization and clustering hybrid model (data mining approaches) as a fuzzy imperialistic competitive algorithm (FICA) and k-means to find the employees’ categories and predict their training requirements.

Prior research that served as an impetus for this paper is discussed. The approach is to apply evolutionary algorithms and clustering hybrid model to improve the training decision system directions.

This paper focuses on how to find a good model for the evaluation of employee profiles. The paper introduces the use of artificial intelligence methods (fuzzy optimization (FICA) and clustering techniques (K-means)) in management. The suggestion and the recommendations were constructed based on the clustering results that represent the employee profiles and reflect their requirements during the training courses. Finally, the paper proved the ability of fuzzy optimization technique and clustering hybrid model in predicting the employee’s training requirements.

This paper evaluates employee profiles based on new directions and expands the implication of clustering view in solving organizational challenges (in TCT for the first time).

This article is going to introduce a modified variant of the imperialist competitive algorithm (ICA). The paper aims to discuss these issues.

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.

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.

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.

The purpose of this paper is to describe imperialist competitive algorithm (ICA), a novel socio-politically inspired optimization strategy for proposing a fuzzy variant of this algorithm. ICA is a meta-heuristic algorithm for dealing with different optimization tasks. The basis of the algorithm is inspired by imperialistic competition. It attempts to present the social policy of imperialisms (referred to empires) to control more countries (referred to colonies) and use their sources. If one empire loses its power, among the others making a competition to take possession of it.

In fuzzy imperialist competitive algorithm (FICA), the colonies have a degree of belonging to their imperialists and the top imperialist, as in fuzzy logic, rather than belonging completely to just one empire therefore the colonies move toward the superior empire and their relevant empires. Simultaneously for balancing the exploration and exploitation abilities of the ICA. The algorithms are used for optimization have shortcoming to deal with accuracy rate and local optimum trap and they need complex tuning procedures. FICA 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 fuzzy logic on it.

Therefore several solution procedures, including ICA, FICA, genetic algorithm, particle swarm optimization, tabu search and simulated annealing optimization algorithm are considered. 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 fuzzy ICA for convex functions with little fluctuations.

The proposed evolutionary algorithm, FICA, can be used in diverse areas of optimization problems where convex functions properties are appeared including, industrial planning, resource allocation, scheduling, decision making, pattern recognition and machine learning (optimization techniques; fuzzy logic; convex functions).