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Quantum beetle antennae search: a novel technique for the constrained portfolio optimization problem

Science China(Information Sciences) 2021年第5期64卷 121-134页

作者： Ameer Tamoor KHAn Xinwei CAO Shuai LI Bin HU vasilios n.katsikis Department of Computing Hong Kong Polytechnic University School of Management Shanghai University School of Information Science and Engineering Lanzhou University Department of Economics Division of Mathematics and Informatics National and Kapodistrian University of Athens

In this paper, we have formulated quantum beetle antennae search(QBAS), a meta-heuristic optimization algorithm, and a variant of beetle antennae search(BAS). We apply it to portfolio selection,a well-known finance problem. Quantum computing is gaining immense popularity among the scientific community as it outsmarts the conventional computing in efficiency and speed. All the traditional computing algorithms are not directly compatible with quantum computers, for that we need to formulate their variants using the principles of quantum mechanics. In the portfolio optimization problem, we need to find the set of optimal stock such that it minimizes the risk factor and maximizes the mean-return of the portfolio. To the best of our knowledge, no quantum meta-heuristic algorithm has been applied to address this problem yet. We apply QBAS on real-world stock market data and compare the results with other meta-heuristic optimization algorithms. The results obtained show that the QBAS outperforms swarm algorithms such as the particle swarm optimization(PSO) and the genetic algorithm(GA).

关键词： quantum computing beetle antennae search portfolio selection optimization finance problem

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BAS-ADAM:An ADAM Based Approach to Improve the Performance of Beetle Antennae Search Optimizer

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IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 461-471页

作者： Ameer Hamza Khan Xinwei Cao Shuai Li vasilios n.katsikis Liefa Liao Department of Computing Hong Kong Polytechnic UniversityHong KongChina School of Management Shanghai UniversityShanghai 201900China Department of Electronics and Electrical Engineering Swansea UniversitySwansea SA18ENUK Department of Economics Division of Mathematics and InformaticsNational and Kapodistrian University of AthensAthens 10679Greece School of Information Engineering Jiangxi University of Science and TechnologyGanzhou 341000China

In this paper,we propose enhancements to Beetle Antennae search(BAS)algorithm,called BAS-ADAIVL to smoothen the convergence behavior and avoid trapping in localminima for a highly noin-convex objective *** achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation(ADAM)update *** proposed algorithm also increases the convergence rate in a narrow valley.A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same *** ADAM is traditionally used with gradient-based optimization algorithms,therefore we first propose a gradient estimation model without the need to differentiate the objective ***,it demonstrates excellent performance and fast convergence rate in searching for the optimum of noin-convex *** efficiency of the proposed algorithm was tested on three different benchmark problems,including the training of a high-dimensional neural *** performance is compared with particle swarm optimizer(PSO)and the original BAS algorithm.

关键词： Adaptive moment estimation(ADAM) Beetle antennae search(BAM) gradient estimation metaheuristic optimization nature-inspired algorithms neural network

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