Quantum-inspired firefly algorithm integrated with cuckoo search for optimal path planning

Harish Kundra, Wasim Khan (), Meenakshi Malik (), Kantilal Pitambar Rane (), Rahul Neware () and Vishal Jain ()
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Harish Kundra: Department of Computer Science and Engineering, Guru Nanak Institutions Technical Campus, Hyderabad, Telangana 501506, India
Wasim Khan: Department of Computer Application, Integral University, Lucknow, Uttar Pradesh 226026, India
Meenakshi Malik: Department of Computer Science and Engineering, Starex University, Gurugram, Haryana 122413, India
Kantilal Pitambar Rane: Department of Electronics and Telecom Engineering, KCE Society’s College of Engineering and Information Technology, Jalgaon, Maharashtra 425001, India
Rahul Neware: Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences, Bergen, Norway
Vishal Jain: Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, Greater Noida, Uttar Pradesh 201310, India

International Journal of Theoretical and Applied Finance (IJTAF), 2022, vol. 33, issue 02, 1-21

Abstract: The firefly algorithm and cuckoo search are the meta-heuristic algorithms efficient to determine the solution for the searching and optimization problems. The current work proposes an integrated concept of quantum-inspired firefly algorithm with cuckoo search (IQFACS) that adapts both algorithms’ expedient attributes to optimize the solution set. In the IQFACS algorithm, the quantum-inspired firefly algorithm (QFA) ensures the diversification of fireflies-based generated solution set using the superstitions quantum states of the quantum computing concept. The cuckoo search (CS) algorithm uses the Lévy flight attribute to escape the QFA from the premature convergence and stagnation stage more effectively than the quantum principles. Here, the proposed algorithm is applied for the application of optimal path planning. Before using the proposed algorithm for path planning, the algorithm is tested on different optimization benchmark functions to determine the efficacy of the proposed IQFACS algorithm than the firefly algorithm (FA), CS, and hybrid FA and CS algorithm. Using the proposed IQFACS algorithm, path planning is performed on the satellite images with vegetation as the focused region. These satellite images are captured from Google Earth and belong to the different areas of India. Here, satellite images are converted into morphologically processed binary images and considered as maps for path planning. The path planning process is also executed with the FA, CS, and QFA algorithms. The performance of the proposed algorithm and other algorithms are accessed with the evaluation of simulation time and the number of cycles to attain the shortest path from defined source to destination. The error rate measure is also incorporated to analyze the overall performance of the proposed IQFACS algorithm over the other algorithms.

Keywords: Quantum computing; meta-heuristic; firefly algorithm; cuckoo search; quantum inspired firefly algorithm; path planning; optimization (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0129183122500188

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