Papers by Keyword: Hybrid Optimization Algorithm

Abstract: A novel efficient transmission, toroidal drive, was introduced into the field of wind power. A novel MW (Million Watt) wind power generation speed-up machine (WPGSM) was designed to replace the gear speed-up machine. The structure scheme of the MW WPGSM was designed and the design platform for the MW WPGSM was developed by UG/OPEN,VC++ and hybrid optimization algorithm based on the unigraphics (UG) software.

Abstract: control strategy is one of the most decisive techniques in Hybrid Electric Bus (HEB) and directly influences the dynamic performance and fuel economy. For achieving the best fuel economy and keeping the battery for a long time, First, power analytic control strategy was built; then, the hybrid optimization algorithm (HOA) based on Multi-island genetic Algorithm (MIGA) and NLPQL was built by ISIGHT software. HOA is adopted in control strategy parameters of HEB optimization. The results show that the best result can be obtained in few iterative times by HOA, the calculation time was reduce by 12 hours, the fuel economy was improved by 12% and find the rules between control strategy parameters and fuel economy the balance of the battery state of charge (SOC).

Abstract: Shuffled frog leaping algorithm (SFLA) is a meta-heuristic algorithm, which combines the social behavior technique and the global information exchange of memetic algorithms. But the SFLA has the shortcoming of low convergence speed while solving complex optimization problems. Particle swarm optimization (PSO) is a fast searching algorithms, but easily falls into the local optimum for the diversity scarcity of particles. In the paper, a new hybrid optimization called SFLA-PSO is proposed, which introduced PSO to SFLA by combining the fast search strategy of PSO and global search strategy of SFLA. Six benchmark functions are selected to compare the performance of SFLA-PSO, basic PSO, wPSO and SFLA. The simulation results show that the proposed algorithm SFLA-PSO possesses outstanding performance in the convergence speed and the precision of the global optimum solution.

Abstract: Vehicle routing optimization problem is one of the major research topics in logistics distribution field. Suitable vehicle routing selection is vital to reduce the logistics cost. The paper presents a hybrid optimization method to solve the vehicle routing problem with time windows. In the hybrid optimization method, discrete particle swarm optimization algorithm is used to assign the customers on routes and simulated annealing (SA) algorithm to avoid becoming trapped in local optimum. The simulation results have shown that the proposed method is feasible and effective for the vehicle routing problem with time windows.

Abstract: In this paper, we introduce a hybrid optimization algorithm with the Branch-and-Bound Method and the Ant Colony Optimization to solve the multi-chromosomal reversal median problem. We convert the large-scale genome into TSP maps at first. Then we use a hybrid optimization algorithm with the Branch-and-Bound Method and the Ant Colony Optimization to solve the problem. In our improved algorithm, we increase the search speed by implement multi-branch parallel search of ACO. Our extensive experiments on simulated datasets show that this median solver is efficient.

Abstract: In this paper, based on glowworm swarm (GS) and artificial fish swarm (AFS) with differential evolution (DE) optimization algorithm, a new hybrid artificial glowworm swarm optimization (HGSO) algorithm is proposed. We use HGSO to solve engineering optimization design problem. The results show that the HGSO has faster convergence, higher precision and is more effective for solving constrained engineering optimization problem.

Abstract: A novel multi-agent particle swarm optimization algorithm (MAI'SO) is proposed for optimal reactive power dispatch and voltage control of power system. The method integrates multi-agent system (MAS) and particle swarm optimization algorithm (PSO). An agent in MAI.SO represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a lattice-like environment, with each agent fixed on a lattice-point. In order to decrease fitness value, quickly, agents compete and cooperate with their neighbors. and they can also use knowledge. Making use of these agent interactions and evolution mechanism of I.SO. MAPSO realizes the purpose of' minimizing the value of' objective function. MAPSO applied for optimal reactive power is evaluated on an IEEE 30-bus power system. It is shown that the proposed approach converges to better solutions much faster than the earlier reported approaches