Opposition-Based Barebones Particle Swarm for Constrained Nonlinear Optimization Problems

Table 3

Comparison results of OBPSO with other three PSO algorithms, where “𝑤/𝑡/𝑙” means that OBPSO wins in 𝑤 functions, ties in 𝑡 functions, and loses in 𝑙 functions, compared with its competitors. The best results among the four algorithms are shown in bold.

Functions	Optimum	NVPSO [4] mean	RVPSO [21] mean	SAVPSO [22] mean	OBPSO mean
𝐺01	−15	−13.871875	−14.7151	−14.4187	−15
𝐺02	−0.803619	−0.336263	−0.74057	−0.413257	−0.79973
𝐺03	−1.0	−1.00484	−1.0034	−1.0025	−1.00126
𝐺04	−30665.539	−30665.5	−30665.5	−30665.5	−30665.5
𝐺05	5126.4981	5126.4957	5202.3627	5241.0549	5126.68
𝐺06	−6961.814	−6961.81	−6961.81	−6961.81	−6961.81
𝐺07	24.306	25.1301	24.989	24.317	24.4196
𝐺08	−0.095825	−0.095825	−0.095825	−0.095825	−0.095825
𝐺09	680.630	680.634430	680.653	680.630	680.630
𝐺10	7049.248	7409.065752	7173.2661	7049.2701	7049.2606
𝐺11	0.750	0.749	0.749	0.749	0.749
𝐺12	−1.0	−1.0	−1.0	−1.0	−1.0
𝐺13	0.05395	0.465217	0.552753	0.681123	0.33837
𝑤/𝑡/𝑙	—	7/5/1	8/5/0	6/6/1	—