Elite Opposition-Based Water Wave Optimization Algorithm for Global Optimization

Table 10

Best results of compression spring by different model.

Researcher(s)	Method	Design variables
		Belegundu [14]	a	0.05		0.315900	14.25000	0.0128334
Arora [15]	b	0.053396	0.399180	9.185400	0.0127303
Coello [16]	GA	0.051480	0.351661	11.632201	0.01270478
Coello [17]	GA	0.05148	0.35166	11.6322	0.0127
Coello and Montes [18]	GA	0.051989	0.363965	10.890522	0.0126810
He et al. [19]	PSO	0.05169040	0.35674999	11.28712599	0.0126652812
Coello and Becerra [20]		0.05	0.3174	14.0318	0.01272
Raj et al. [21]		0.05386200	0.41128365	8.68437980	0.01274840
Hedar and Fukushima [22]		0.051742503409	0.358004783455	11.2139073627	0.012665285
He and Wang [23]	PSO	0.051728	0.357644	11.244543	0.0126747
Montes and Coello [24]		0.051643	0.355360	11.397926	0.012698
Omran and Salman [25]		0.0516837458	0.3565898352	11.2964717107	0.0126652375
Aragón et al. [26]	T-	0.05162	0.35511	11.3845	0.01267
Akay and Karaboga [27]	ABC	0.051749	0.358179	11.203763	0.012665
Gandomi et al. [28]	BA	0.05169	0.35673	11.2885	0.01267
Gandomi [29]		NA	NA	NA	0.012665
Baykasoğlu and Ozsoydan [30]	FA	0.0516674837	0.3561976945	11.3195613646	0.0126653049
Present study	EOBWWO	0.05169826	0.356939073	11.2760014	0.012665234

a: mathematical optimization technique; b: numerical optimization technique; c: evolutionary programming; d: evolutionary computational technique; e: simulated annealing; f: evolution strategies; g: chaotic search, opposition-based learning, differential evolution, and quantum mechanics; h: TCA is the T-cell algorithm; i: interior search algorithm; NA: there is no relevant data.