The term‘optimization’refers to the process of maximizing the beneficial attributes of a mathematical function or system while minimizing the unfavorable ones.The majority of real-world situations can be modelled as...The term‘optimization’refers to the process of maximizing the beneficial attributes of a mathematical function or system while minimizing the unfavorable ones.The majority of real-world situations can be modelled as an optimization problem.The complex nature of models restricts traditional optimization techniques to obtain a global optimal solution and paves the path for global optimization methods.Particle Swarm Optimization is a potential global optimization technique that has been widely used to address problems in a variety of fields.The idea of this research is to use exponential basis functions and the particle swarm optimization technique to find a numerical solution for the Sine-Gordan equation,whose numerical solutions show the soliton form and has diverse applications.The implemented optimization technique is employed to determine the involved parameter in the basis functions,which was previously approximated as a random number in the work reported till now in the literature.The obtained results are comparable with the results obtained in the literature.The work is presented in the form of figures and tables and is found encouraging.展开更多
Particle Swarm Optimization,a potential swarm intelligence heuristic,has been recognized as a global optimizer for solving various continuous as well as discrete optimization problems.Encourged by the performance of G...Particle Swarm Optimization,a potential swarm intelligence heuristic,has been recognized as a global optimizer for solving various continuous as well as discrete optimization problems.Encourged by the performance of Gompertz PSO on a set of continuous problems,this works extends the application of Gompertz PSO for solving binary optimization problems.Moreover,a new chaotic variant of Gompertz PSO namely Chaotic Gompertz Binary Particle Swarm Optimization(CGBPSO)has also been proposed.The new variant is further analysed for solving binary optimization problems.The new chaotic variant embeds the notion of chaos into GBPSO in later stages of searching process to avoid stagnation phenomena.The efficiency of both the Binary PSO variants has been tested on different sets of Knapsack Problems(KPs):0-1 Knapsack Problem(0-1 KP)and Multidimensional Knapsack Problems(MKP).The concluding remarks have made on the basis of detailed analysis of results,which comprises the comparison of results for Knapsack and Multidimensional Knapsack problems obtained using BPSO,GBPSO and CGBPSO.展开更多
文摘The term‘optimization’refers to the process of maximizing the beneficial attributes of a mathematical function or system while minimizing the unfavorable ones.The majority of real-world situations can be modelled as an optimization problem.The complex nature of models restricts traditional optimization techniques to obtain a global optimal solution and paves the path for global optimization methods.Particle Swarm Optimization is a potential global optimization technique that has been widely used to address problems in a variety of fields.The idea of this research is to use exponential basis functions and the particle swarm optimization technique to find a numerical solution for the Sine-Gordan equation,whose numerical solutions show the soliton form and has diverse applications.The implemented optimization technique is employed to determine the involved parameter in the basis functions,which was previously approximated as a random number in the work reported till now in the literature.The obtained results are comparable with the results obtained in the literature.The work is presented in the form of figures and tables and is found encouraging.
文摘Particle Swarm Optimization,a potential swarm intelligence heuristic,has been recognized as a global optimizer for solving various continuous as well as discrete optimization problems.Encourged by the performance of Gompertz PSO on a set of continuous problems,this works extends the application of Gompertz PSO for solving binary optimization problems.Moreover,a new chaotic variant of Gompertz PSO namely Chaotic Gompertz Binary Particle Swarm Optimization(CGBPSO)has also been proposed.The new variant is further analysed for solving binary optimization problems.The new chaotic variant embeds the notion of chaos into GBPSO in later stages of searching process to avoid stagnation phenomena.The efficiency of both the Binary PSO variants has been tested on different sets of Knapsack Problems(KPs):0-1 Knapsack Problem(0-1 KP)and Multidimensional Knapsack Problems(MKP).The concluding remarks have made on the basis of detailed analysis of results,which comprises the comparison of results for Knapsack and Multidimensional Knapsack problems obtained using BPSO,GBPSO and CGBPSO.