In nature-inspired metaheuristic algorithms, two key components are local intensification and global diversification, and their interaction can significantly affect the efficiency of a metaheuristic algorithm. However, there is no rule for how to balance these important components. In this paper, we provide a first attempt to give some theoretical basis for the optimal balance of exploitation a...

Ant Colony Optimization (ACO) algorithms belong to class of metaheuristic algorithms, where a search is made for optimized solution rather than exact solution, based on the knowledge of the problem domain. ACO algorithms are iterative in nature. As the iteration proceeds, solution converges to the optimized solution. In this paper, we propose new updation mechanism based on clustering technique...

Of all of the challenges which face the selection of relevant features for predictive data mining or pattern recognition modeling, the adaptation of computational intelligence techniques to feature selection problem requirements is one of the primary impediments. A new improved metaheuristic based on Greedy Randomized Adaptive Search Procedure (GRASP) is proposed for the problem of Feature Sele...

This paper presents a methodology for integrating machine learning techniques into metaheuristics solving combinatorial optimization problems. Namely, we propose general framework neighbor generation in metaheuristic search. We first define an efficient neighborhood structure constructed by applying transformation to selected subset of variables from the current solution. Then, key proposed is ...

This paper presents the Clustering Search (CS) as a new hybrid metaheuristic, which works in conjunction with other metaheuristics, managing the implementation of local search algorithms for optimization problems. Usually the local search is costly and should be used only in promising regions of the search space. The CS assists in the discovery of these regions by dividing the search space into...

Over the past decades, several techniques have been employed to improve the applicability of the metaheuristic optimization methods. One of the solutions for improving the capability of metaheuristic methods is the hybrid of algorithms. This study proposes a new optimization algorithm called HPBA which is based on the hybrid of two optimization algorithms; Big Bang-Big Crunch (BB-BC) inspired b...

This paper proposes a modified sine cosine algorithm (MSCA) for discrete sizing optimization of truss structures. The original sine cosine algorithm (SCA) is a population-based metaheuristic that fluctuates the search agents about the best solution based on sine and cosine functions. The efficiency of the original SCA in solving standard optimization problems of well-known mathematical function...

The Capacitated Centred Clustering Problem (CCCP) consists in partitioning a set of n points into p disjoint clusters with a known capacity. Each cluster is specified by a centroid. The objective is to minimize the total dissimilarity within each cluster, such that a given capacity limit of the cluster is not exceeded. This paper presents a solution procedure for the CCCP, using the hybrid meta...

The efficiency of any metaheuristic algorithm largely depends on the way of balancing local intensive exploitation and global diverse exploration. Studies show that bat algorithm can provide a good balance between these two key components with superior efficiency. In this paper, we first review some commonly used metaheuristic algorithms, and then compare the performance of bat algorithm with t...

We intend to show in this paper that metaheuristics based on GRASP and VNS can give good results for a generalization of the Traveling Salesman Problem (TSP) called Traveling Purchaser Problem (TPP) when compared with the Tabu Search algorithm proposed by Voss [13] for this algorithm, concerning the quality of solutions in similar execution times. In these algorithms, several simple and well kn...