A Quantum-inspired Artificial Bee Colony algorithm for numerical optimisation

Amira Bouaziz, A. Draa, S. Chikhi
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引用次数: 16

Abstract

A Quantum-inspired Artificial Bee Colony algorithm (QABC) for numerical optimisation is proposed in this paper. A hybridisation is made between two paradigms: Artificial Bee Colony (ABC) optimisation on one hand and Quantum Computing (QC) principles on the other hand. Some quantum concepts including the quantum bit, states superposition and quantum interference are used to enhance the diversity and computing capabilities of standard ABC algorithm. The experimental results obtained from testing the proposed algorithm on a set of numerical benchmark functions have shown that the QABC is competitive to quantum swarms and evolutionary approaches. It outperforms conventional evolutionary algorithms and quantum-inspired particle swarm algorithm.
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一种量子启发的数值优化人工蜂群算法
提出了一种量子启发的用于数值优化的人工蜂群算法(QABC)。在人工蜂群(ABC)优化和量子计算(QC)原理两种范式之间进行了杂交。为了提高标准ABC算法的多样性和计算能力,引入了量子比特、态叠加和量子干涉等量子概念。在一组数值基准函数上的实验结果表明,QABC算法与量子群算法和进化算法相比具有一定的竞争力。它优于传统的进化算法和量子启发的粒子群算法。
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