Table 2Test problems search and initialization ranges for the PSO variants.Experiment1. The purpose of this experiment was to compare LDIW-PSO with CDIW-PSO [2]. All the test problems described previously were used in this experiment, except f1. The dimension for f5 was 2, while it was 30 for others. The maximum numbers most of iterations were set to 1500 with swarm size of 20, and the experiment was repeated 500 times. Stated in Table 3 are the set goals (criteria) of success for each of the problems.Table 3Goals for the test problems in CDIW-PSO.Experiment2. The purpose of this experiment was to compare LDIW-PSO with REPSO [7]. All the test problems in Table 1 except f1 were used. The dimension for f5 was 2, while it was 10 for others.
The performances of the algorithms were considered at different number of iterations as shown in Section 5, in line with what is recorded in the literature [7]. The swarm size used was 30, and the experiment was repeated 50 times. Experiment3. The purpose of this experiment was to compare LDIW-PSO with DAPSO [13]. Test problems f1 ? f3 were used with four different problem dimensions of 20, 30, 40, and 50. The maximum number of iterations and swarm size was set to 3000 and 30, respectively, and the experiment was repeated 50 times.Experiment4. The purpose of this experiment was to compare LDIW-PSO with APSO [5]. f2, f3, and f4 were used as test problems with three different problem dimensions of 10, 20, and 30. The respective maximum numbers of iterations associated with these dimensions are 1000, 1500, and 2000, respectively.
The experiment was carried out over three different swarm sizes, 20, 40, and 80 for each problem dimension, and the experiment was repeated 30 times.Experiment5. This experiment compared LDIW-PSO with DLPSO2 [11]. All the test problems except f4 were used in the experiment with two different problem dimensions of 2 (for f3 and f5) and 30 (for f1, f2, and f6). The maximum number of iterations was set to 2000 and swarm sizes to 20, and the experiment was repeated 20 times. 5. Results and DiscussionsPresented in Tables Tables44�C8 are the results obtained for all the experiments. The results for all the competing PSO variants were obtained from the respective referenced papers, and they are presented here the way they were recorded. Thus, the recording of the results for LDIW-PSO was patterned after them.
In each of the tables, bold values represent the best results. In the tables, mean best fitness (solution) is a measure of Anacetrapib the precision that the algorithm can get within a given number of iterations, standard deviation (Std. Dev.) is a measure of the algorithm’s stability and robustness, while success rate (SR) [31] is the rate at which an algorithm obtains optimum fitness result in the criterion range during a given number of independent runs.Table 4Experimental results for LDIW-PSO compared with CDIW-PSO.Table 8Experimental results for LDIW-PSO compared with DLPSO2.