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Table 2. A collection of hybridized DE-PSO algorithms. 4.8.3. Hybridization of PSO Using Simulated Annealing (SA)

Table 2 A collection of hybridized DE-PSO algorithms. 4.8.3. Hybridization of PSO Using Simulated Annealing (SA)

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Table 1. A collection of hybridized GA-PSO algorithms.
Table 1. A collection of hybridized GA-PSO algorithms.
4.8.2. Hybridization of PSO Using Differential Evolution (DE) Table 1. Cont.
4.8.2. Hybridization of PSO Using Differential Evolution (DE) Table 1. Cont.
4.8.4. Hybridization of PSO Using Ant Colony Optimization (ACO) Table 3. A collection of hybridized SA-PSO algorithms.
4.8.4. Hybridization of PSO Using Ant Colony Optimization (ACO) Table 3. A collection of hybridized SA-PSO algorithms.
4.8.5. Hybridization of PSO Using Cuckoo Search (CS) Table 4. A collection of hybridized PSO-ACO algorithms.
4.8.5. Hybridization of PSO Using Cuckoo Search (CS) Table 4. A collection of hybridized PSO-ACO algorithms.
Table 5. A collection of hybridized PSO-CS algorithms. 4.8.6. Hybridization of PSO Using Artificial Bee Colony (ABC)
Table 5. A collection of hybridized PSO-CS algorithms. 4.8.6. Hybridization of PSO Using Artificial Bee Colony (ABC)
Table 6. A collection of hybridized PSO-ABC algorithms. 4.8.7. Hybridization of PSO Using Other Social Metaheuristic Approaches The following section discusses some instances where the Particle Swarm Optimization algorithm has been hybridized with other commonly used social metaheuristic optimization algorithms for use in an array of engineering applications. Common techniques include Artificial Immune Systems [139-142], Bat Algorithm [143], Firefly Algorithm [144] and Glow Worm Swarm Optimization Algorithm [145].
Table 6. A collection of hybridized PSO-ABC algorithms. 4.8.7. Hybridization of PSO Using Other Social Metaheuristic Approaches The following section discusses some instances where the Particle Swarm Optimization algorithm has been hybridized with other commonly used social metaheuristic optimization algorithms for use in an array of engineering applications. Common techniques include Artificial Immune Systems [139-142], Bat Algorithm [143], Firefly Algorithm [144] and Glow Worm Swarm Optimization Algorithm [145].
Table 7. A collection of PSO algorithms hybridized with other approaches such as AIS, BA, FA and GSO. 4.9. Parallelized Implementations of PSO
Table 7. A collection of PSO algorithms hybridized with other approaches such as AIS, BA, FA and GSO. 4.9. Parallelized Implementations of PSO
Table 9. 3D Plots of the Benchmark Functions. The results in Table 10 provide a high-level, intuitive understanding of benchmarking experiments using PSO and a few of its variants. In order to comment on the performance of the algorithms, statistical significance tests with an appropriate confidence level (generally alpha = 0.01 or 0.05) are commonly carried out.
Table 9. 3D Plots of the Benchmark Functions. The results in Table 10 provide a high-level, intuitive understanding of benchmarking experiments using PSO and a few of its variants. In order to comment on the performance of the algorithms, statistical significance tests with an appropriate confidence level (generally alpha = 0.01 or 0.05) are commonly carried out.
Table 8. Benchmark Functions f1-/8. Table 9 shows the 3-dimensional representations of the benchmark functions (f 1-8).
Table 8. Benchmark Functions f1-/8. Table 9 shows the 3-dimensional representations of the benchmark functions (f 1-8).
Note: NR: Not Reported. Table 10. Performances of Some Variants of PSO on f1-f8.
Note: NR: Not Reported. Table 10. Performances of Some Variants of PSO on f1-f8.
Related topics:
Optimization (Mathematics)Swarm IntelligenceMetaheuristics (Operations Research)Ant Colony OptimizationDifferential EvolutionCuckoo Search
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