“…Finally, some existing algorithms were selected for comparison with IDPGA. These algorithms performed well in solving all or some of these six problems, including SC (society and civilization) [71], PSO-DE (a novel hybrid algorithm) [72], DEDS (differential evolution with dynamic stochastic selection for constrained optimization) [73], HEAA (constrained optimization based on hybrid evolutionary algorithm and adaptive constrainthandling technique) [74], CS (cuckoo search algorithm) [16], AHA (artificial hummingbird algorithm) [75], GA2 (using co-evolution to adapt the penalty factors of a fitness function incorporated in a genetic algorithm) [76], GA3 (a dominance-based selection scheme to incorporate constraints into the fitness function of a genetic algorithm) [77], CA (a cultural algorithm that uses domain knowledge) [78], CPSO (a co-evolutionary particle swarm optimization approach) [79], ABC (artificial bee colony) [80], GA5 (a new approach to handle constraints using evolutionary algorithms in which the new technique treats constraints as objectives and uses a multiobjective optimization approach to solve the re-stated singleobjective optimization problem) [81], GeneAS I (genetic adaptive search I) [82], GeneAS II (genetic adaptive search II) [76], DMO (dwarf mongoose optimization algorithm) [83], AOA (the arithmetic optimization algorithm) [84], SSA (squirrel search algorithm) [85], SCA (sine cosine algorithm) [86], GWO (grey wolf optimizer) [87], CPSOGSA (hybrid constriction coefficient based PSO with gravitational search algorithm (GSA)) [83], Hsu and Liu's algorithm [88], Rao's algorithm [89], GSA-GA (a new hybrid GSA-GA algorithm) [90], AO (aquila optimizer) [12], GTO (giant trevally optimizer) [13] and TLCO (termite life cycle optimizer) [14]. The best simulation results of IDPGA and other results reported in the current literature are listed in Table 9.…”