Nonconvex Economic Dispatch Using Particle Swarm Optimization with Time Varying Operators

Table 4

Comparison results for case study 2.

Method	Best fuel cost; ($/hr)	Average fuel cost; ($/hr)	Worst fuel cost; ($/hr)	CPU time; (s)
SQP [28]	122904.4243	124883.7692	126585.2290	10.80
EP-SQP [29]	122323.9700	122379.6300	—	997.73
PSO-SQP [29]	122094.6700	122245.2500	—	733.97
PSO-LRS [12]	122035.7946	123382.0000	125740.6300	31.61
NPSO [12]	121704.7391	122221.3697	122995.0976	8.23
NPSO-LRS [12]	121664.4308	122981.5913	122209.3185	20.74
DEC-SQP [30]	121741.9800	123367.6500	125397.9600	925.63
DEC(2)-SQP(1) [28]	121741.9793	122295.1278	122839.2941	14.26
ACO [31]	121532.4100	121606.4500	121679.6400	52.45
FCASO [32]	121516.4700	122082.5900	—	145.2
SOH-PSO [14]	121501.1400	121853.5700	122446.3000	—
TSARGA [33]	121463.0700	122928.3100	124296.5400	696.0
CPSO-SQP [34]	121458.5400	122028.1600	—
GA-PS-SQP [29]	121458.0000	122039.0000	—	46.98
ABC [35]	121441.0300	121995.8200	—	30.02
CCPSO [22]	121412.5362	121445.3269	121525.4934	19.3
ICA-PSO [7]	121422.1000	—	—	139.9
DE/BBO [36]	121420.8948	—	—	12
HHS [37]	121415.5920	121615.8544	—	16.39
IPSO [2]	121412.8660	121509.5223	121546.8420	42.89
NAPSO [8]	121412.5700	—	—	12.7
CSA [38]	121412.5355	121520.4106	121810.2538	3.03
Proposed PSO	121412.5355	121432.3215	121564.3454	9.99