- India
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Research Interests: Engineering, Evolutionary Computation, Genetic Algorithms, Differential Evolution, Bio-Inspired Systems, and 15 moreGenetic Algorithm, Probability, Probability Distribution & Applications, Mathematical Sciences, Numerical Optimization, Evolutionary Algorithm, Self Organization, Evolution Strategy, Parameter Tuning, Parameter Optimization, Bio-inspired Computation, Evolutionary Programming, Information Sciences, General Population, and Objective function
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In the present study we propose a new hybrid version of Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms called Hybrid DE or HDE for solving continuous global optimization problems. In the proposed HDE... more
In the present study we propose a new hybrid version of Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms called Hybrid DE or HDE for solving continuous global optimization problems. In the proposed HDE algorithm, information sharing mechanism of PSO is embedded in the contracted search space obtained by the basic DE algorithm. This is done to maintain a balance between the two antagonist factors; exploration and exploitation thereby obtaining a faster convergence. The embedding of swarm directions to the basic DE algorithm is done with the help of a "switchover constant" called α which keeps a record of the contraction of search space. The proposed HDE algorithm is tested on a set of 10 unconstrained benchmark problems and four constrained real life, mechanical design problems. Empirical studies show that the proposed scheme helps in improving the convergence rate of the basic DE algorithm without compromising with the quality of solution.
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Differential evolution (DE) algorithms are commonly used metaheuristics forglobal optimization, but there has been very little research done on the generation of theirinitial population. The selection of the initial population in a... more
Differential evolution (DE) algorithms are commonly used metaheuristics forglobal optimization, but there has been very little research done on the generation of theirinitial population. The selection of the initial population in a population-based heuristicoptimization method is important, since it affects the search for several iterations and oftenhas an influence on the final solution. If no a priori information about the optima isavailable, the initial population is often selected randomly using pseudorandom numbers.In this paper, we have investigated the effect of generating the initial population withoutusing the conventional methods like computer generated random numbers or quasi randomsequences. We have applied non linear simplex method in conjugation of pseudorandomnumbers to generate initial population for DE. Proposed algorithm is named as NSDE(using non linear simplex method), is tested on a set of 20 benchmark problems with boxconstraints, taken from literature and the ...
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Differential evolution (DE) is a population based evolutionary search algorithm widely used for solving optimization problems. In the present article we investigate the application of parent-centric approach on the performance of... more
Differential evolution (DE) is a population based evolutionary search algorithm widely used for solving optimization problems. In the present article we investigate the application of parent-centric approach on the performance of classical DE, without tampering with the basic structure of DE. The parent-centric approach is embedded in the mutation phase of DE. We propose two versions of (DE) called differential evolution with parent-centric crossover (DEPCX) and differential evolution with probabilistic parent-centric crossover (ProDEPCX) in order to improve the performance of classical DE. The proposed algorithms are validated on a test bed of ten benchmark functions and the numerical results are compared with basic DE and a modified version called trigonometric differential evolution (TDE). Empirical analysis of numerical results on the benchmark problems show that the performance of proposed versions is either at par or better in comparison to TDE and basic DE in terms of convergence rate and quality of fitness function value.
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In this paper we have proposed three variations of the Basic Particle Swarm Optimization (BPSO), called GWPSO+ED, GWPSO+GD and GWPSO+UD. The novelty of the approach is the combination a newly developed inertia weight with different... more
In this paper we have proposed three variations of the Basic Particle Swarm Optimization (BPSO), called GWPSO+ED, GWPSO+GD and GWPSO+UD. The novelty of the approach is the combination a newly developed inertia weight with different probability distributions. The numerical results of the modified versions are compared with the BPSO. Simulations show that the proposed versions are comparable with BPSO and in most of the cases give superior performance.
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Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, non-linear and multi-modal objective functions. DE has been consistently ranked as one of the best search algorithm for solving global... more
Differential Evolution (DE) is a novel evolutionary approach capable of handling non-differentiable, non-linear and multi-modal objective functions. DE has been consistently ranked as one of the best search algorithm for solving global optimization problems in several case studies. This paper presents a simple and modified hybridized Differential Evolution algorithm for solving global optimization problems. The proposed algorithm is a hybrid of Differential Evolution (DE) and Evolutionary Programming (EP). Based on the generation of initial population, three versions are proposed. Besides using the uniform distribution (U-MDE), the Gaussian distribution (G-MDE) and Sobol sequence (S-MDE) are also used for generating the initial population. Empirical results show that the proposed versions are quite competent for solving the considered test functions.
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Research Interests: Engineering, Electrical Engineering, Linear Programming, Power System, Differential Evolution, and 16 moreComparative Study, Particle Swarm Optimization, Electric Power Systems, Power System Protection, Numerical Simulation, Non Linear Programming, Honey bee, OPTIMIZATION TECHNIQUE, Mixed Integer Programming, Overcurrent Protection, Electrical And Electronic Engineering, Particle Swarm Optimizer, LINEAR PROGRAM, Constrained Optimization, Quadratic Interpolation, and Objective function
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Differential Evolution (DE) has emerged as a powerful tool for solving optimization problems in the last few years. However, the convergence rate of DE still does not meet all the requirements, and attempts to speed up differential... more
Differential Evolution (DE) has emerged as a powerful tool for solving optimization problems in the last few years. However, the convergence rate of DE still does not meet all the requirements, and attempts to speed up differential evolution are considered necessary. ...
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This paper evaluates the performance of three Particle Swarm Optimization (PSO) algorithms, namely attraction-repulsion based PSO (ATREPSO), Quadratic Interpolation based PSO (QIPSO) and Gaussian Mutation based PSO (GMPSO). Whereas all... more
This paper evaluates the performance of three Particle Swarm Optimization (PSO) algorithms, namely attraction-repulsion based PSO (ATREPSO), Quadratic Interpolation based PSO (QIPSO) and Gaussian Mutation based PSO (GMPSO). Whereas all the algorithms are guided by the diversity of the population to search the global optimal solution of a given optimization problem, GMPSO uses the concept of mutation and QIPSO uses the reproduction operator to generate a new member of the swarm. We tested the variants of PSO on ten standard benchmark functions and compared the results with classical PSO algorithm. Also, the performance of all algorithms is tested on two engineering design problems. The numerical results show that all the algorithms outperform the classical particle swarm optimization by a remarkable difference.
Research Interests: Management Information Systems, Quality Management, Ant Colony Optimization, Technology Management, Engineering Design, and 10 moreParticle Swarm Optimization, Stochastic processes, Optimization Problem, Global Optimization, Interpolation, Particle Swarm, Contemporary Management Applications, Meta Heuristics, Quadratic Interpolation, and Application Software
Research Interests: Computational Modeling, Genetic Algorithms, Space Technology, Particle Swarm Optimization, Stochastic processes, and 11 moreProbability Distribution & Applications, Global Optimization, Robustness, Search Space, Random Number Generation, Higher Dimensions, CEC, Particle Swarm Optimizer, Random Sequences, Computational Method, and Random Numbers
This chapter presents some of the recent modified variants of Particle Swarm Optimization (PSO). The main focus is on the design and implementation of the modified PSO based on diversity, Mutation, Crossover and efficient Initialization... more
This chapter presents some of the recent modified variants of Particle Swarm Optimization (PSO). The main focus is on the design and implementation of the modified PSO based on diversity, Mutation, Crossover and efficient Initialization using different distributions and Low-discrepancy sequences. These algorithms are applied to various benchmark problems including unimodal, multimodal, noisy functions and real life applications in engineering fields. The effectiveness of the algorithms is discussed.
I. INTRODUCTION TWO competing goals that govern the design of global search methods are exploration and exploitation. Exploration is important to ensure global reliability, ie, every part of the domain is searched enough to provide a... more
I. INTRODUCTION TWO competing goals that govern the design of global search methods are exploration and exploitation. Exploration is important to ensure global reliability, ie, every part of the domain is searched enough to provide a reliable estimate of the global optimum; ...
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Peer-to-peer (P2P) topology has significant influence on the performance, search efficiency and functionality, and scalability of the application. In this paper, we present a Genetic Agorithm (GA) approach to the problem of... more
Peer-to-peer (P2P) topology has significant influence on the performance, search efficiency and functionality, and scalability of the application. In this paper, we present a Genetic Agorithm (GA) approach to the problem of multi-objective Neighbor Selection (NS) in P2P Networks. The encoding representation is from the upper half of the peer-connection matrix through the undirected graph, which reduces the search space dimension. Experiment results indicate that GA usually could obtain better results than Particle Swarm Optimization (PSO).
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This paper presents a new variant of Particle Swarm Optimization algorithm named QPSO for solving global optimization problems. QPSO is an integrated algorithm making use of a newly defined, multiparent, quadratic crossover operator in... more
This paper presents a new variant of Particle Swarm Optimization algorithm named QPSO for solving global optimization problems. QPSO is an integrated algorithm making use of a newly defined, multiparent, quadratic crossover operator in the Basic Particle Swarm Optimization (BPSO) algorithm. The comparisons of numerical results show that QPSO outperforms BPSO algorithm in all the twelve cases taken in this study.
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In this paper we have presented a new variant of Basic Particle Swarm Optimization (BPSO) algorithm named QIPSO for solving global optimization problems. The QIPSO algorithm makes use of a multiparent, quadratic crossover/reproduction... more
In this paper we have presented a new variant of Basic Particle Swarm Optimization (BPSO) algorithm named QIPSO for solving global optimization problems. The QIPSO algorithm makes use of a multiparent, quadratic crossover/reproduction operator defined by us in the BPSO algorithm. We have compared it with Basic Particle Swarm Optimization and the numerical results show that QIPSO outperforms the BPSO