Abstract
Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA.
Highlights
Finding an optimal solution in high-dimensional complex space is a common issue in many engineering fields [1]
A total of nine algorithms, including IPSO, Particle Swarm Optimization (PSO), Biogeography-based optimization (BBO), Crow Search Algorithm (CSA), Moth Flame Optimization (MFO), Fireworks Algorithm (FWA), Grey Wolf Optimizer (GWO), augmented grey wolf optimizer (AGWO), and enhanced grey wolf optimizer (EGWO), were selected for comparison with the FWGWO algorithm proposed in this paper
The results show that in most cases the performance of the FWGWO algorithm is significantly improved when compared with other algorithms
Summary
Finding an optimal solution in high-dimensional complex space is a common issue in many engineering fields [1]. In [23], Alomoush proposed a hybrid algorithm based on Gray Wolf Optimizer (GWO) and Harmony Search (HS). In [27], ZHU proposed a novel hybrid algorithm based on the grey wolf optimizer (GWO) and differential evolution (DE). This algorithm is tested on 23 benchmark functions and a non-deterministic polynomial hard problem. A global optimization algorithm combining biogeography-based optimization (BBO) with fireworks algorithm (FWA) is proposed in [28]. This paper proposes a new hybrid algorithm which combines the exploration ability of FWA with the exploitation ability of GWO to increase the convergence characteristics.
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