Multi-swarm optimization algorithm for dynamic optimization problems using forking

2008 ◽  
Author(s):  
Hongfeng Wang ◽  
Na Wang ◽  
Dingwei Wang
Keyword(s):  
Dynamic Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Dynamic Optimization Problems ◽  
Swarm Optimization

Reverse Search Particle Swarm Optimization to Deal with Dynamic Optimization Problems

2014 ◽  
Vol 571-572 ◽  
pp. 245-251
Author(s):  
Li Chen ◽  
Wei Jiang Wang ◽  
Lei Yao
Keyword(s):  
Particle Swarm Optimization ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
Fitness Function ◽  
Dynamic Environment ◽  
Particle Swarm ◽  
Dynamic Optimization Problems ◽  
Swarm Optimization ◽  
Promising Area ◽  
Coarse Search

Multiswarm approaches are used in many literatures to deal with dynamic optimization problems (DOPs). Each swarm tries to find promising areas where usually peaks lie and many good results have been obtained. However, steep peaks are difficult to be found with multiswarm approaches , which hinders the performance of the algorithm to be improved furtherly. Aiming at the bottleneck, the paper introduces the idea of sequential niche technique to traditional multiswarm approach and thus proposes a novel algorithm called reverse space search multiswarm particle swarm optimization (RSPSO) for DOPs. RSPSO uses the information of the peaks found by coarse search of traditional multiswarm approach to modify the original fitness function. A newly generated subswarm - reverse search subswarm evolves with the modified fitness function, at the same time, other subswarms using traditional mltiswarm approach still evolve. Two kinds of subswarm evolve in cooperation. Reverse search subswarm tends to find much steeper peak and so more promising area where peaks lie is explored. Elaborated experiments on MPB show the introduction of reverse search enhances the ability of finding peaks , the performance of RSPSO significantly outperforms traditional multiswarm approaches and it has better robustness to adapt to dynamic environment with wider-range change severity.

scholarly journals Improved Seagull Optimization Algorithm Combined with an Unequal Division Method to Solve Dynamic Optimization Problems

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1037
Author(s):  
Le Xu ◽  
Yuanbin Mo ◽  
Yanyue Lu ◽  
Jiang Li
Keyword(s):  
Dynamic Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Difficult Problem ◽  
Attack Behavior ◽  
Dynamic Optimization Problems ◽  
Group Approach ◽  
Unequal Division ◽  
Fitness Value

The numerical solution of the dynamic optimization problem is often sought for chemical processes, but the discretization of control variables is a difficult problem. Firstly, based on the analysis of the seagull optimization algorithm, this paper introduces the cognitive part in the process of a seagull’s attack behavior to make the group approach the best position. Secondly, the algorithm adds the mechanism of natural selection, where the fitness value is used to sort the population, and the best half is used to replace the worst half, so as to find out the optimal solution. Finally, the improved seagull optimization algorithm (ISOA) is combined with the unequal division method to solve dynamic optimization problems. The feasibility of the method is verified by three practical examples of dynamic optimization in chemical industry.

The Use of a Random Algorithm for Dynamic Optimization of Mechanisms

1977 ◽  
Vol 99 (1) ◽  
pp. 153-156 ◽  
Author(s):  
B. Z. Sandler
Keyword(s):  
Spectral Theory ◽  
Dynamic Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Random Processes ◽  
Dynamic Optimization Problems ◽  
Vibrating System ◽  
Random Algorithm ◽  
Number Combination

This paper describes the following two kinds of dynamic optimization problems applied to mechanisms and their solution by using the spectral theory of random processes: (1) One has to choose such parameters of three-mass vibrating system which will insure the minimum vibration of the most important element. (2) The possibility is envisioned of achieving output motion with minimal errors in a gearing mechanism, without increasing the accuracy of the wheels. It is suggested to achieve this effect by the choice of optimal teeth number combination. A randomized optimization algorithm is considered for these aims.

Multi-Thread Memory Particle Swarm Optimization for Dynamic Optimization Problems

2014 ◽  
Vol 571-572 ◽  
pp. 232-236
Author(s):  
Li Chen ◽  
Ju Ming Liu ◽  
Jun Mei Ouyang
Keyword(s):  
Particle Swarm Optimization ◽  
Dynamic Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
External Memory ◽  
Dynamic Optimization Problems ◽  
Good Information ◽  
Swarm Optimization ◽  
Future Evolution ◽  
Global Optima

This paper proposes a multi-thread-memory particle swarm optimization (MTM-PSO) for dynamic optimization problems (DOPs). It introduces multi-thread memory to multi-swarm approaches to deal with DOPs. External memory and multi-swarm approaches are adopted. The best particles near the peaks in each environment are saved in the memory according to storage strategy of update and implement. Multi-thread memory makes full use of local and global optima found in each environment. It makes the good information in the previous evolution transfer to the current population adequately. Using the multi-thread memory and its storage strategy, the particles in the memory are always the best information of local and global optima found until in the current envionment. The information will benefit the future evolution. Experiments on the moving peak benchmark (MPB) for different numbers of peaks testiy MTM-PSO has better performance than dynamic optimization algorithms only with multi-swarm approach.

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