Introducing a Multi Population Algorithm based on PSO for Solving Dynamic Optimization Problems

Authors

1 Department of Electrical Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran

2 Young Researchers and Elite Clubs, Yasooj Branch, Islamic Azad University, Yasooj, Iran

3 Department of Mathematic, Yasooj Branch, Islamic Azad University, Yasooj, Iran

4 Department of Computer Engineering, Noorabad Mamasani Branch, Islamic Azad University, Noorabad Mamasani, Fars, Iran

5 Young Researchers and Elite Clubs, Noorabad Mamasani Branch, Islamic Azad University, Noorabad Mamasani, Fars, Iran

Abstract

Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track the changing optima over time. Typical examples include recognition of the moving objects in changing background; constructing financial trading models in various changing market conditions; data mining in continuously updating databases; scheduling problems with dynamic available resources; vehicle routing in traffic networks of dynamic traffic flow, etc. which requires the optimization algorithms to be able to find and track the changing optima efficiently over time. Among various algorithms for dynamic optimization, particle swarm optimization algorithms (PSOs) are attracting more and more attentions in recent years. We have proposed three algorithms based on PSO for dynamic environments. The proposed algorithms are the multi-swarm algorithms. In the traditional research it is shown that the multi swarm algorithms are suitable for creating diversity in the environments. In this paper, we propose an adaptive strategy for dynamic envirounments. The adaptive strategy increases convergence speed of the algorithm. The proposed algorithm uses several suitable operators for increasing the efficiency of the algorithm. One of the operators in the proposed algorithm is the trajectory control operator.  The proposed algorithm has the best results rather than the state-of-the-art.

Keywords

References

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TABRIZ JOURNAL OF ELECTRICAL ENGINEERING
Volume 48, Issue 3 - Serial Number 85
November 2018
Pages 1405-1423

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