Multimodal optimization, which aims at locating multiple optimal solutions within the search space, is inherently a difficult problem. This work proposes an adaptive memetic differential evolution algorithm with niching competition and supporting archive strategies to tackle In proposed algorithm, strategy designed competitively employ niches according their potentials by encouraging high poten...

The work presented in this article concerns the classification of numeric data representing voxels of multimodal RM-Imaging. The procedure is partially supervised and it's not made any supposition on the number of classes and their correspondent's prototypes. The problem of initialization of the prototypes as well as their number is transformed in an optimization problem, besides the procedure ...

Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimizat...

Microarray data usually contain a large number of genes, but a small number of samples. Feature subset selection for microarray data aims at reducing the number of genes so that useful information can be extracted from the samples. Reducing the dimension of data sets further helps in improving the computational efficiency of the learning model. In this paper, we propose a modified algorithm bas...

This article proposes an improved particle swarm optimization (PSO) with suggested parameter setting “Selective Particle Regeneration”. To evaluate its reliability and efficiency, this approach is applied to multimodal function optimizing tasks. 12 benchmark functions were tested, and results are compared with those of PSO and GA-PSO. It shows the proposed method is both robust and suitable for...

Differential Evolution (DE) is a population-based stochastic global optimization technique that requires the adjustment of a very few parameters in order to produce results. However, the control parameters involved in DE are highly dependent on the optimization problem; in practice, their fine-tuning is not always an easy task. The self-adaptive differential evolution (SADE) variants are those ...

 The clonal selection principle is used to explain the basic features of an adaptive immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective antigens. In this paper, we propose a computational i...

This paper presents multimodal function optimization, using a nature-inspired glowworm swarm optimization (GSO) algorithm, with applications to collective robotics. GSO is similar to ACO and PSO but with important differences. A key feature of the algorithm is the use of an adaptive local-decision domain, which is used effectively to detect the multiple optimum locations of the multimodal funct...

ant colony optimisation (aco) algorithm and adaptive refinement mechanism are used in this paper for solution of optimization problems. many of the real engineering problems are، however، of continuous nature and finding their solution by discrete ant based algorithms requires discretisation of the decision variables in which affected the convergence and performance of the algorithm. in this pa...