A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey

2012 ◽  
Vol 53 (1) ◽  
pp. 75-83 ◽  
Author(s):  
Mustafa Servet Kıran ◽  
Eren Özceylan ◽  
Mesut Gündüz ◽  
Turan Paksoy
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Demand ◽  
Ant Colony Algorithm ◽  
Hybrid Approach ◽  
Particle Swarm ◽  
Ant Colony ◽  
Swarm Optimization

Review of Task Scheduling Algorithm Research in Cloud Computing

2014 ◽  
Vol 926-930 ◽  
pp. 3236-3239 ◽  
Author(s):  
Mei Geng Huang ◽  
Zhi Qi Ou
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Ant Colony Algorithm ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Slow Convergence

The cloud computing task scheduling field representative algorithms was introduced and analyzed : genetic algorithm, particle swarm optimization, ant colony algorithm. Parallelism and global search solution space is the characteristic of genetic algorithm, genetic iterations difficult to proceed when genetic individuals are very similar; Particle swarm optimization in the initial stage is fast, slow convergence speed in the later stage ; Ant colony algorithm optimization ability is good, slow convergence speed in its first stage; Finally, the summary and prospect the future research direction.

An Improved PSO-ACO Algorithm for Solving Large-Scale TSP

2010 ◽  
Vol 143-144 ◽  
pp. 1154-1158 ◽  
Author(s):  
Ai Jia Ouyang ◽  
Yong Quan Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Large Scale ◽  
Ant Colony Algorithm ◽  
Shortest Paths ◽  
Particle Swarm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Improved Pso

In this paper, an improved particle swarm optimization-ant colony algorithm (PSO-ACO) is presented by inserting delete-crossover strategy into it for the shortcoming which PSO-ACO can’t solve the large-scale TSP. The experiments results show that the PSO-ACO has better performance than ant colony algorithm (ACO) on searching the shortest paths, error and robustness for the TSP.

scholarly journals Decision of Multimodal Transportation Scheme Based on Swarm Intelligence

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Kai Lei ◽  
Xiaoning Zhu ◽  
Jianfei Hou ◽  
Wencheng Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
Multimodal Transportation ◽  
Better Than

In this paper, some basic concepts of multimodal transportation and swarm intelligence were described and reviewed and analyzed related literatures of multimodal transportation scheme decision and swarm intelligence methods application areas. Then, this paper established a multimodal transportation scheme decision optimization mathematical model based on transportation costs, transportation time, and transportation risks, explained relevant parameters and the constraints of the model in detail, and used the weight coefficient to transform the multiobjective optimization problems into a single objective optimization transportation scheme decision problem. Then, this paper is proposed by combining particle swarm optimization algorithm and ant colony algorithm (PSACO) to solve the combinatorial optimization problem of multimodal transportation scheme decision for the first time; this algorithm effectively combines the advantages of particle swarm optimization algorithm and ant colony algorithm. The solution shows that the PSACO algorithm has two algorithms’ advantages and makes up their own problems; PSACO algorithm is better than ant colony algorithm in time efficiency and its accuracy is better than that of the particle swarm optimization algorithm, which is proved to be an effective heuristic algorithm to solve the problem about multimodal transportation scheme decision, and it can provide economical, reasonable, and safe transportation plan reference for the transportation decision makers.

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