Abstract
Particle Swarm Optimization used to solve a continu ous problem and has been shown to perform well however, binary version still has some problems. In order to solve these problems a new technique call ed New Binary Particle Swarm Optimization using Immunity-Clonal Algorithm (NPSOCLA) is proposed This Algorithm proposes a new updating strategy to update the position vector in Binary Particle Swarm Optimization (BPSO), which further combined with Immunity-Clonal Algorithm to improve the optimization ability. To investigate the performanc e of the new algorithm, the multidimensional 0/1 knapsack problems are used as a test benchmarks. The experiment results demonstrate that the New Binar y Particle Swarm Optimization with Immunity Clonal Algorithm, found the optimum solution for 53 of the 5 8 multidimensional 0/1knapsack problems.
Highlights
INTRODUCTIONWhere, (i) is the index of the particle and (t) is the time. In Equation (1), the velocity (v) of particle (i) at a time
This James Kennedy and Russell Eberhart introduced a Particle Swarm Optimization (PSO) in 1995 (Eberhart and Kennedy, 1995; Kennedy et al, 2001) by simulate a bird swarm
PSO depending on three steps which are repeated until some stopping condition is met, the first step is to Evaluate the fitness of each particle specify the individual best position and global position ending with update velocity and position of each particle using the following equations:
Summary
Where, (i) is the index of the particle and (t) is the time. In Equation (1), the velocity (v) of particle (i) at a time (1) where, (c1) is a cognitive coefficient that usually close to 2 and affects the size of step the particle takes toward the (pbest) and (r1) is a random value between 0 and 1 cause the particle to move in semi direction toward (pbest). The third term (c2r2(Gbesti(t)-pi(t))) called social effect; it is responsible for allowing the particle to follow (Gbest) the best position the swarm has found so far where (c2) is a social coefficient that usually close to 2 and affects the size of step the particle takes toward (Gbest) and (r2) is a random value between 0 and 1 cause the particle to move in semi direction toward (Gbest) once the velocity is calculated, the position updated by Equation (2)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
