Table 1.
List of acronyms.
| Acronyms | The full name of an acronym |
|---|---|
| MOPs [1] | Multiobjective optimization problems |
| PSO [7] | Particle swarm optimization |
| MOPSOs | Multiobjective particle swarm optimization algorithms |
| MOEAs | Multiobjective evolutionary algorithms |
| GCDMOPSO | Multiobjective particle swarm optimization based on cosine distance mechanism and game strategy |
| MOPSO [9] | Handling multiple objectives with particle swarm optimization |
| NSGA-II [10] | A fast and elitist multiobjective genetic algorithm |
| PAES [11] | Approximating the nondominated front using the Pareto archived evolution strategy |
| SMPSO [13] | A new PSO-based metaheuristic for multiobjective optimization |
| MMOPSO [14] | A novel multiobjective particle swarm optimization with multiple search strategies |
| MOEA/D [15] | A multiobjective evolutionary algorithm based on decomposition |
| SDMOPSO [17] | A novel smart multiobjective particle swarm optimization using decomposition |
| dMOPSO [19] | A multiobjective particle swarm optimizer based on decomposition |
| MOPSONN [20] | A fast multiobjective particle swarm optimization algorithm based on a new archive updating mechanism |
| IGD [22] | Inverted generational distance |
| NMPSO [23] | Particle swarm optimization with a balance able fitness estimation for many-objective optimization problems |
| MOPSOCD [24] | An effective use of crowding distance in multiobjective particle swarm optimization |
| MPSO/D [18] | A new multiobjective particle swarm optimization algorithm based on decomposition |
| NSGA-III [25] | An evolutionary many-objective optimization algorithm using reference point-based nondominated sorting approach, part I: solving problems with box constraints |
| MOEAIGDNS [26] | A multiobjective evolutionary algorithm based on an enhanced inverted generational distance metric |
| SPEAR [27] | A strength Pareto evolutionary algorithm based on reference direction for multiobjective and many-objective optimization |
| SPEA2 [28] | Improving the strength Pareto evolutionary algorithm |
| IBEA [29] | Indicator-based selection in multiobjective search |
| N | The population size |
| M | The number of objectives |
| D | Dimension of the decision variable |
| FEs | The maximum number of evaluations |
| p c | Crossover probability |
| p m | Mutation probability |
| SBX | Simulated binary crossover |
| PM | Polynomial-based mutation |
| η c | The distribution indexes of SBX |
| η m | The distribution indexes of PM |
| F | Parameters set by the author in differential evolution |
| CR | Parameters set by the author in differential evolution |
| div | The division network number of cells |
| pbest | Personal best particle |
| gbest | Global best particle |