Nanda Dulal Jana

Particle Swarm Optimization with adaptive polynomial mutation

Particle Swarm Optimization (PSO) has shown its good search ability in many optimization problem. But PSO easily gets trapped into local optima while dealing with complex problems. In this work, we proposed an improved PSO, namely PSO-APM, in which adaptive polynomial mutation strategy is employed on global best particle with the hope that it will help the particles jump out local optima. In this work, we carried out our experiments on 8 well-known benchmark problems. Finally the results are compared with classical PSO and PSO with power mutation (PMPSO).

Selection of appropriate metaheuristic algorithms for protein structure prediction in AB off-lattice model

Protein structure prediction (PSP) from its primary sequence is a challenging task in computational biology. PSP is an optimization problem that determines the stable or native structure with minimum free energy. Several researchers have applied various heuristic algorithms and/or their variants to solve this problem. However, the mechanism to select a particular algorithm is not known a priori. Fitness landscape analysis (FLA) is a technique to determine the characteristics of a problem or its structural features based on which the most appropriate algorithm can be recommended for solving the problem. The aim of this study is two-fold while considering the PSP problem. Firstly, the structural features are determined by using the standard FLA techniques and secondly, the performance of some of the well-known optimization algorithms are analyzed based on the structural features as an illustration of the usefulness of the former research agenda. In this paper, we determine structural features of the PSP problem by analyzing the landscapes generated by using the quasi-random sampling technique and city block distance. Comprehensive simulations are carried out on both artificial and real protein sequences in 2D and 3D AB off-lattice model. Numerical results indicate that the complexity of the PSP problem increases with protein sequence length. We calculate the Pearson correlation coefficient between the FLA measures, separately for 2D and 3D off-lattice model and significant differences are identified among the measures. Six well-known real-coded optimization algorithms are evaluated over the same set of protein sequences and the performances are subsequently analyzed based on the structural features. Finally, we suggest the most appropriate algorithms for solving different classes of PSP problem.

Levy distributed parameter control in differential evolution for numerical optimization

Differential evolution (DE) algorithm is a population based stochastic search technique widely applied in scientific and engineering fields for global optimization over real parameter space. The performance of DE algorithm highly depends on the selection of values of the associated control parameters. Therefore, finding suitable values of control parameters is a challenging task and researchers have already proposed several adaptive and self-adaptive variants of DE. In the paper control parameters are adapted by levy distribution, named as Levy distributed DE (LdDE) which efficiently handles exploration and exploitation dilemma in the search space. In order to assure a fair comparison with existing parameter controlled DE algorithms, we apply the proposed method on number of well-known unimodal, basic and expanded multimodal and hybrid composite benchmark optimization functions having different dimensions. The empirical study shows that the proposed LdDE algorithm exhibits an overall better performance in terms of accuracy and convergence speed compared to five prominent adaptive DE algorithms.

Improved Bees Algorithm for Protein Structure Prediction Using AB Off-Lattice Model

Protein Structure Prediction (PSP) using sequence of amino acids is a multimodal optimization problem and belongs to NP hard class. Researchers and scientists put their efforts to design efficient computational intelligent algorithm for solving this kind of problem. Bees Algorithm (BA) is a swarm intelligence based algorithm inspired by the foraging behaviour of honey bees colony, already exhibits its potential ability for solving optimization problems. However, it may produce premature convergence when solving PSP like problems. To prevent this situation, Adaptive Polynomial Mutation based Bees Algorithm (APM-BA) has been proposed in this paper for predicting protein structure in 2D AB off-lattice model. In this strategy, each of best scout bees are mutated with adaptive polynomial mutation technique when their performances are no more improve during execution phase. The experiments are conducted on artificial and real protein sequences and numerical results show that the proposed algorithm has strong ability for solving PSP problem having minimum energy.

Artificial Neural Network Training Using Differential Evolutionary Algorithm for Classification

In this work, we proposed a method of artificial neural network learning using differential evolutionary(DE) algorithm. DE with global and local neighborhood based mutation(DEGL) algorithm is used to search the synaptic weight coefficients of neural network and to minimize the learning error in the error surface.DEGL is a version of DE algorithm in which both global and local neighborhood-based mutation operator is combined to create donor vector.The proposed method is applied for classification of real-world data and experimental results show the efficiency and effectiveness of the proposed method and also a comparative study has been made with classical DE algorithm.

Constrained function optimization using PSO with polynomial mutation

Constrained function optimization using particle swarm optimization (PSO) with polynomial mutation is proposed in this work. In this method non-stationary penalty function approach is adopted and polynomial mutation is performed on global best solution in PSO. The proposed method is applied on 6 benchmark problems and obtained results are compared with the results obtained from basic PSO. The experimental results show the efficiency and effectiveness of the method.

Bi level kapurs entropy based image segmentation using particle swarm optimization

In the field of Image Processing, Image segmentation is a low level but important task in entire image understanding system which divides an image into its multiple disjoint regions based on homogeneity. In most of the machine vesion and high level image understanding application this is one of the important steps. Till date different techniques of image segmentation are available and hence There exists a huge survey literature in different approaches of Image Segmentation. Selection of image segmentation technique is highly problem specific. There is no versatile algorithm which is applicable for all kinds of images. Optimization based image segmentation is not explored much which can be applied to reduce complexity of the problem. The aim of the paper is to search for an optimized threshold value for Image Segmentation using Particle Swarm Optimization (PSO) algorithm where fitness function is designed based on entropy of the image.

Particle Swarm Optimization with Lévy Flight and Adaptive Polynomial Mutation in gbest Particle

In this paper, particle swarm optimization (PSO) with levy flight is proposed. PSO is a population based global optimization algorithm has faster convergence but often gets stuck in local optima due to lack of diversity in the population. In the proposed method, levy flight is applied on a percentage of particles excluding global best particle to create diversity in population. Adaptive polynomial mutation is applied on global best (gbest) particle to get it out from the trap in local optima. The method is applied on well-known benchmark unconstrained functions and results are compares with classical PSO. Form the experimental result, it has been observed that the proposed method performs better than classical PSO.

Modified Artificial Bee Colony Algorithm using differential evolution and polynomial mutation for real-parameter optimization

Artificial Bee Colony (ABC) is a swarm based stochastic search algorithm inspired by the foraging behavior of honeybees. Due to the simplicity of implementation and promising optimization capability, ABC is successfully applied to solve wide class of scientific and engineering optimization problems. But, it has problems of premature convergence and trapping in local optima. In this paper, to enhance the performance of ABC, we have proposed a modified version of ABC algorithm using Differential Evolution (DE) and Polynomial Mutation (PM) called DE-PM-ABC. The comparison with ABC by Karaboga [1], MABC [27] by Liu et al. using some benchmark functions of CEC 2005 demonstrates that our approach achieves a good trade-off between exploration and exploitation and thus obtains better global optimization result and faster convergence speed.

Protein Structure Prediction in 2D HP Lattice Model Using Differential Evolutionary Algorithm

Protein Structure Prediction (PSP) is a challenging problem in bioinformatics and computational biology research for its immense scope of application in drug design, disease prediction, name a few. Developing a suitable optimization technique for predicting the structure of proteins has been addressed in the paper, using Differential Evolutionary (DE) algorithm applied in the square 2D HP lattice model. In the work, we concentrate on handling infeasible solutions and modify control parameters like population size (NP), scale factor (F), crossover ratio (CR) and mutation strategy of the DE algorithm to improve its performance in PSP problem. The proposed method is compared with the existing methods using benchmark sequence of protein databases, showing very promising and effective performance in PSP problem.