Optimal location of capacitor in radial distribution network using Chemical Reaction Optimization algorithm

In this paper, a chaotic chemical reaction optimization approach to receding horizon control is proposed for multiple unmanned aerial vehicles formation. To keep the coordinated formation with the minimum cost value in all of the planning horizons, the unmanned aerial vehicles formation problem is converted to online optimization problems by giving a receding horizon control scheme. The chemical reaction optimization algorithm is a new optimization inspired by the nature of chemical reactions. Furthermore, the chaotic operator is utilized to help the optimization algorithm avoid of the local optimum and find better optimal parameters. Comparative results show that our proposed method outperforms over traditional particle swarm optimization algorithm.

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Modified chemical reaction optimization and its application in engineering problems

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2021354 ◽

2021 ◽

Vol 18 (6) ◽

pp. 7143-7160

Author(s):

Shijing Ma ◽

◽

Yunhe Wang ◽

Shouwei Zhang ◽

Keyword(s):

Chemical Reactions ◽

Chemical Reaction ◽

Optimization Algorithm ◽

Optimization Algorithms ◽

Differential Evolution Algorithm ◽

Search Space ◽

Evolutionary Optimization ◽

Chemical Reaction Optimization ◽

Reaction Optimization ◽

Proposed Model

<abstract><p>Chemical Reaction Optimization (CRO) is a simple and efficient evolutionary optimization algorithm by simulating chemical reactions. As far as the current research is concerned, the algorithm has been successfully used for solving a number of real-world optimization tasks. In our paper, a new real encoded chemical reaction optimization algorithm is proposed to boost the efficiency of the optimization operations in standard chemical reactions optimization algorithm. Inspired by the evolutionary operation of the differential evolution algorithm, an improved search operation mechanism is proposed based on the underlying operation. It is modeled to further explore the search space of the algorithm under the best individuals. Afterwards, to control the perturbation frequency of the search strategy, the modification rate is increased to balance between the exploration ability and mining ability of the algorithm. Meanwhile, we also propose a new population initialization method that incorporates several models to produce high-quality initialized populations. To validate the effectiveness of the algorithm, nine unconstrained optimization algorithms are used as benchmark functions. As observed from the experimental results, it is evident that the proposed algorithm is significantly better than the standard chemical reaction algorithm and other evolutionary optimization algorithms. Then, we also apply the proposed model to address the synthesis problem of two antenna array synthesis. The results also reveal that the proposed algorithm is superior to other approaches from different perspectives.</p></abstract>

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