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- W2377484211 abstract "Particle swarm optimization(PSO) algorithm is mainly used to find global solutions of continuous variables optimization problems.In this paper,the penalty function approach to handle the discrete variables is employed,in which mixed discrete optimization problem: min f(x),s.t.gk(x)≤0,k=1,2,…l,xLi≤xi≤xUi,i=1,2,…,m,xm+j∈Dj,Dj=(dj,1,dj,2,…,dj,qj),j=1,2,…,n is handled as continuous one: min F(x),s.t.xLi≤xi≤xUi,i=1,2,…,m,dj,1≤xm+j≤dj,qj,j=1,2…,n.Standard PSO algorithm will likely fall into local optimal solution and exist premature convergence.Tabu search(TS) algorithm has good hill-climbing ability and can escape from the local optimal solution and turn to other parts of the solution space.A neighborhood structure is designed and a hybrid tabu search and particle swarm optimization(TS-PSO) algorithm is proposed,which has memory ability and efficient hill-climbing capability.Simulation results on Rosenbrocks function and pressure vessel design show that the disadvantage of getting in the local best point of standard PSO is overcome effectively and the ability of global optimality is toned up." @default.
- W2377484211 created "2016-06-24" @default.
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- W2377484211 date "2011-01-01" @default.
- W2377484211 modified "2023-09-23" @default.
- W2377484211 title "A Hybrid Tabu Search and Particle Swarm Optimization Algorithm for Mixed Discrete Optimization Problems" @default.
- W2377484211 hasPublicationYear "2011" @default.
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