Multi-strategy artificial bee colony based on multiple population for coverage optimisation

Abstract

In order to overcome the shortcomings of weak local search ability and slow convergence speed for the standard artificial bee colony algorithm, this paper proposes an improved multi-strategy artificial bee colony algorithm based on multiple populations (IMSABC). Firstly, the employed bees are randomly divided into three subgroups, corresponding to three evolutionary strategies. If the candidate solution obtained from searching is inferior to the current honey source, the bee is randomly assigned to other subgroups and the search strategy is changed. In this way, it not only facilitates the information exchange between populations, but also balances the global search and local development capabilities of the algorithm since the three search strategies have different characteristics. Secondly, by imitating the particle swarm algorithm, the search strategy of the following bees is improved by using the abundant information contained in the current global optimal honey source and random neighbour honey source. The simulation results of twelve benchmark test functions and 28 CEC2013 functions show that the performance of this algorithm has significant advantages compared with many similar improved algorithms. In order to improve the unreasonable distribution of sensor nodes and improve the network coverage, the above algorithm is applied to optimise the coverage of wireless sensor networks and achieve better optimisation effect.

Swarm Intelligence-Based Performance Optimization for Mobile Wireless Sensor Networks: Survey, Challenges, and Future Directions

“…In order to reduce the cost of sensor deployment, Su et al [131] proposed an optimized strategy for mobile wireless sensor networks node deployment on the basis of dynamic ant colony algorithm. In order to overcome the shortcomings of weak local search ability and slow convergence speed for the standard artificial bee colony algorithm, Su et al [132] proposed an improved multi-strategy artificial bee colony algorithm based on multiple populations. For example in [133] the authors focused on redundant network nodes, short life cycle and network effective coverage as optimization goals, and then introduced the inverse Gaussian mutation algorithms on AFSA, made the improved algorithm to solve the model, and got the optimal coverage scheme for mobile wireless sensor networks.…”