Fig 1.
Schematic representation of GA mutation and crossover.
Fig 2.
Typical flow chart for genetic algorithm.
Fig 3.
Schematic representation of PSO velocity components.
Fig 4.
Flow chart for particle swarm optimization.
Fig 5.
Flow chart for PGPHEA algorithm.
Fig 6.
Flow chart for the proposed hybrid algorithm.
Fig 7.
Pseudocode of the proposed hybrid algorithm.
Table 1.
Continuous benchmark problems of Set A.
Table 2.
Global optima for objective functions f1 –f14.
Fig 8.
Comparison of algorithmic performance in solving the continuous problems of Set A: a) average error, b) maximum error in predicting the global optimum.
Table 3.
Comparison of average error in finding the global optimum of functions f1-f14.
Table 4.
Comparison of maximum error in finding global optimum of functions f1-f14.
Table 5.
Wilcoxon signed-rank-test (p ≥ 0.05) for average error in continuous problems of Set A.
Table 6.
Algorithm Ranking for continuous problems of Set A.
Fig 9.
Graphical representation of the evolution of fitness with CPU time spent for Set A continuous problems (f1-f14).
Fig 10.
Comparison of algorithmic performance in solving continuous problems of Set B (CEC 2017): a) average error, b) maximum error in predicting the global optimum.
Table 7.
Comparison of average error in finding the global optimum for CEC 2017 problems.
Table 8.
Comparison of maximum error in finding global optimum for CEC 2017 problems.
Table 9.
Algorithm Ranking for CEC 2017 problems.
Table 10.
Wilcoxon signed-rank-test (p ≥ 0.05) for average error/ in CEC 2017 problems.
Fig 11.
Comparison of algorithmic performance in solving TLP: a) average relative error, b) maximum relative error in predicting the optimal distance.
Table 11.
Comparison of average relative distance error for traveling salesman problems.
Table 12.
Comparison of maximum distance error for traveling salesman problems.
Table 13.
Wilcoxon signed-rank-test (p ≥ 0.05) for average error in continuous problems.
Table 14.
Discrete problem algorithm ranking.