An Adaptive Shrinking Grid Search Chaotic Wolf Optimization Algorithm Using Standard Deviation Updating Amount

Table 1

Arithmetic configuration.

Order	Name	Configuration
1	Genetic algorithm [16]	The crossover probability is set at 0.7; the mutation probability is set at 0.01, while the generation gap is set at 0.95
2	Particle swarm optimization	Value of individual acceleration: 2, value of weighted value of initial time: 0.9, value of weighted value of convergence time: 0.4, limit individual speed at 20% of the changing range
3	LWPS	Migration step size stepa: 1.5, raid step size stepb: 0.9, siege threshold r0: 0.2, upper limit of siege step size stepcmax = 10^6, lower limit of siege step size stepcmin = 10^−2, updating number of wolves m: 5
4	CWOA	Amount of campaign wolves q: 5, searching direction h: 4, upper limit of search Hmax: 15, migration step size stepa0: 1.5, raid step size stepb: 0.9, siege threshold r0: 0.2, value of siege step size stepc0: 1.6, updating amount of wolves m: 5
5	ASGS-CWOA	Migration step-size stepa0: 1.5, upper limit of siege step-size stepcmax = 1e6, lower limit of siege step size step cmin = 1e − 40; upper limit number of iteration T: 600; number of wolf population N: 50