Solving Constrained Global Optimization Problems by Using Hybrid Evolutionary Computing and Artificial Life Approaches

Table 1

The parameter settings for the RGA-PSO and AIA-PSO algorithms.

Methods	Parameter settings	Search space
The external RGA	𝑝𝑐=1
		𝑝𝑚,RGA=0.15	[𝜒^𝑙,𝜒^𝑢] = [0.1,1]
	psRGA=10	[𝑐^𝑙1,𝑐^𝑢1] = [0.1,2]
	𝑔max,RGA=3	[𝑐^𝑙2,𝑐^𝑢2] = [0.1,2]
	The external AIA	𝑝𝑟𝑡=0.9	[𝜌^𝑙,𝜌^𝑢] = [1×10^9,1×10^11]
rs=10		[𝑝^𝑙𝑚,PSO,𝑝^𝑢𝑚,PSO]=[0.1,0.5]
𝑔max,AIA=3
The internal PSO algorithm	psPSO=100	[𝑥^𝑙𝑛, 𝑥^𝑢𝑛] for a CGO problem
	𝑔max,PSO=3500 for TPs 1–4
	𝑔max,PSO=3000 for TPs 5–13

[𝜒^𝑙,𝜒^𝑢]: the lower and upper boundaries of parameter 𝜒.
[𝑐^𝑙1,𝑐^𝑢1]: the lower and upper boundaries of parameter 𝑐₁.
[𝑐^𝑙2,𝑐^𝑢2]: the lower and upper boundaries of parameter 𝑐₂.
[𝜌^𝑙,𝜌^𝑢]: the lower and upper boundaries of parameter 𝜌.
[𝑝^𝑙𝑚,_PSO,𝑝^𝑢𝑚,_PSO]: the lower and upper boundaries of 𝑝_𝑚,PSO for the internal PSO algorithm 𝑝_𝑚,PSO.