Quantum Artificial Bee Colony Algorithm for Numerical Function Optimization

Abstract

The Artificial Bee Colony (ABC) algorithm is a swarm intelligence based algorithm, which simulate the foraging behavior of honey bee colonies. It has been widely applied to solve the real-world problem. However, ABC has good exploration but poor exploitation abilities, and its convergence speed is also an issue in some cases. In order to overcome these issues, this paper presents a new metaheuristic algorithm called Quantum Artificial Bee Colony (QABC) algorithm for global optimization problems inspired by quantum physics concepts. Simulations are conducted on a suite of unimodal/multimodal continuous benchmark functions. The results demonstrate the good performance of the QABC algorithm in solving complex numerical optimization problems when compared with other popular algorithms. General TermsBio-inspired algorithm, Numerical optimization, metaheuristics.

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“…In this work, the quantum artificial bee colony algorithm (QABCA) is applied to solve the first step model [31, 32]. The QABCA integrates the artificial bee colony algorithm (ABCA) with quantum computing theories and by virtues of the advantages of the ABCA and quantum computing, it can reduce the computation complexity and improve the convergence speed of the concerned problem significantly [31, 32].…”

“…In this work, the quantum artificial bee colony algorithm (QABCA) is applied to solve the first step model [31, 32]. The QABCA integrates the artificial bee colony algorithm (ABCA) with quantum computing theories and by virtues of the advantages of the ABCA and quantum computing, it can reduce the computation complexity and improve the convergence speed of the concerned problem significantly [31, 32]. The main procedures for solving the first step CSN optimisation model using the QABCA are as follows: (i) Set the iteration limit and set (7) as the fitness function of the QABCA. (ii) Solve the first step CSN optimisation model and calculate the fitness value. (iii) If the sub‐network optimised by the QABCA is connected, go next. (iv) If the sub‐network optimised by the QABCA is not connected, make it connected using the shortest path between the sub‐sub‐networks.…”