A New PSO Algorithm with Crossover Operator for Global Optimization Problems
نویسندگان
چکیده
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.
منابع مشابه
An improved particle swarm optimization with a new swap operator for team formation problem
Formation of effective teams of experts has played a crucial role in successful projects especially in social networks. In this paper, a new particle swarm optimization (PSO) algorithm is proposed for solving a team formation optimization problem by minimizing the communication cost among experts. The proposed algorithm is called by improved particle optimization with new swap operator (IPSONSO...
متن کاملParticle Swarm Optimization Using Crossover Operator
Particle swarm optimization (PSO) has been widely used mainly due to its simple concept and its ability to converge to reasonable solution fast. However, this algorithm is always inefficient while optimizing complex global optimization problems because it is easy to be trapped into local optima. Researches into the combination of evolutionary operators with PSO is one of the most significant an...
متن کاملThe movement of the particles is influenced by two factors using information from iteration-to-iteration as well
Particle Swarm Optimization (PSO) has been extensively used in recent years for the optimization of nonlinear optimization problems. Two of the most popular variants of PSO are PSO-W (PSO with inertia weight) and PSO-C (PSO with constriction factor). Typically particles in swarm use information from global best performing particle, gbest and their own personal best, pbest. Recently, studies hav...
متن کاملA New Hybrid Flower Pollination Algorithm for Solving Constrained Global Optimization Problems
Global optimization methods play an important role to solve many real-world problems. Flower pollination algorithm (FP) is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, a new hybrid optimization method called hybrid flower pollination algorithm (FPPSO) is proposed. The method combines the standard flower pollination algorithm (FP) with the par...
متن کاملParticle Swarm Optimization Using Blended Crossover Operator
In recent days, Swarm Intelligence plays an important role in solving many real life optimization problems. Particle Swarm Optimization (PSO) is swarm intelligence based search and optimization algorithm which is used to solve global optimization problems. But due to lack of population diversity and premature convergence it is often trapped into local optima. We can increase diversity and preve...
متن کامل