Accurate and comprehensible implementation of multiple metaheuristics.
- Python 3.6.9
- NumPy 1.17.3 (vector math)
- Deap 1.3 (only to import benchmark functions)
- Ant colony optimization for continuous domains (ACOr). Socha, 2006. - Adaptive elitism level ACOr (AELACOr). Costa, 2020. - Adaptive generation dispersion ACOr (AGDACOr). Costa, 2020. - Bi-adaptive ACOr (MAACOr). Costa, 2020. - Simulated annealing (SA). Kirkpatrick, 1983. - Adaptive crystallization factor SA (ACFSA). Martins, 2012. - Particle swarm optimization (PSO). Kennedy, 1995. - Adaptive inertia weight PSO (AIWPSO). Nickabadi, 2011.
- base_metaheuristic.py
- simulated_annealing.py
- particle_swarm_optimization.py
- ant_colony_for_continuous_domains.py
- apply_metaheuristics.py - Uses all metaheuristics to search for minimum values of a given benchmark (mostly used for verification purposes)
- lin_sig_exp_experiment.py - Extracts results for AELACOr and AGDACOr considering different maps from the colony success rate to parameter values
- lin_sig_exp_stats.py - Displays summary statistics for the results from lin_sig_exp_experiment.py
- metaheuristic_test_functions_experiment.py - Collects results for a given metaheuristics in a set of test function instances.
- metaheuristic_results_tables.py - Displays summary statistics and statistical significance of the results from metaheuristic_test_functions_experiment.py
- metaheuristic_results_plot.py - Plots the average search history of each metaheuristic, considering results from metaheuristic_test_functions_experiment.py
Costa, V. O. and Müller, M. F. (2020). "On the Multiple Possible Adaptive Mechanisms of the Continuous Ant Colony Optimization". 9th Brazilian Conference on Intelligent Systems, BRACIS (2020).