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Quantum Entanglement inspired Grey Wolf optimization algorithm and its application

EVOLUTIONARY INTELLIGENCE 2023年第4期16卷 1097-1114页

作者： Deshmukh, Nagraj Vaze, Rujuta Kumar, Rajesh Saxena, Akash Malaviya Natl Inst Technol Jaipur Dept Elect Engn Jaipur Rajasthan India Swami Keshvanand Inst Technol Dept Elect Engn Management & Gramothan Jaipur Rajasthan India

Meta-heuristic optimization algorithms are becoming increasingly popular for their simplicity and efficiency. Grey wolf Optimizer (GWO) is one such effective algorithm that was proposed recently. It has been researched extensively owing to its impressive characteristics-easy to understand and implement, few parameters to be tuned, capability to balance exploration and exploitation and high solution accuracy. But in solving high dependence or complex optimization problems, GWO can stagnate into local optima owing to poor exploration strategy and can converge prematurely. To overcome these drawbacks of GWO, we propose Quantum Entanglement enhanced Grey Wolf Optimizer (QEGWO). Quantum Entanglement is particularly useful in significantly improving the treatment of multimodal and high dependence problems. One more element-local search-is used and is helpful in the search intensification. The QEGWO algorithm is benchmarked on 12 standard benchmark functions (unimodal as well as multimodal) and results are compared with some existing variants of GWO. Further, it is also benchmarked on Congress of Evolutionary computing-2019 (CEC'19) benchmark set consisting of 10 shifted and rotated functions. Further, the applicability of the QEGWO is tested over harmonic estimator design problem. A bench of smooth and noisy functions is employed to test estimation accuracy of QEGWO. The results reveal that QEGWO performs significantly better as compared to other GWO variants.

关键词： Optimization Metaheuristic algorithms Grey Wolf optimizer Quantum Entanglement high-dependency problems

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Development and application of Quantum Entanglement inspired Particle Swarm Optimization

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KNOWLEDGE-BASED SYSTEMS 2021年 219卷 106859-106859页

作者： Vaze, Rujuta Deshmukh, Nagraj Kumar, Rajesh Saxena, Akash Malaviya Natl Inst Technol Jaipur Dept Elect Engn Jaipur Rajasthan India Swami Keshvanand Inst Technol Management & Gramot Dept Elect Engn Jaipur Rajasthan India

Particle Swarm Optimization has been extensively researched and applied to tackle optimization problems due to the ease in implementation and less number of parameters to be tuned. But particle swarm optimization (PSO) algorithm gets trapped into local optimum in high-dimensional space and it is inefficient in solving optimization problems which show high dependency. To overcome the above problems without compromising the advantages of PSO, this paper proposes Quantum Entanglement inspired Particle Swarm Optimization (QEPSO). QEPSO incorporates entangled states in its Q-bits to efficiently solve high-dependency problems and uses quantum local search to accelerate the optimization process. The proposed algorithm is tested on several standard benchmark functions and is also further benchmarked on IEEE Congress of Evolutionary computing (CEC 2017) benchmark set. The performance of QEPSO is compared with existing variants of PSO and some other popular algorithms. The results show that QEPSO outperforms other algorithms and is especially useful in high dimensional problems. Finally it is used for a real-life application of Multi-level Image Segmentation where eight gray-scale standard test images were used. The performance of QEPSO was superior to the other algorithms as it gave better results with high stability and quick convergence. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Metaheuristic algorithms Particle Swarm Optimization Quantum Entanglement high-dependency problems

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