A Collection of User-Friendly Heuristic Optimization Algorithms
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Resource Overview
A comprehensive set of practical and user-friendly heuristic optimization algorithms, including non-adaptive algorithms, simulated annealing-based population algorithms, basic genetic algorithms, differential evolution algorithms, and particle swarm optimization. Additionally features the Sacred Algorithm which integrates all these optimization operators with occasional algorithm swapping between different populations.
Detailed Documentation
This paper introduces an outstanding collection of user-friendly heuristic optimization algorithms. The implemented algorithms encompass non-adaptive methods, population-based algorithms derived from simulated annealing, fundamental genetic algorithms, differential evolution techniques, and particle swarm optimization methods. Furthermore, we present the Sacred Algorithm that strategically combines all these optimization operators, occasionally performing algorithm exchanges between different populations. Through practical implementation of these algorithms, developers can effectively solve diverse optimization problems while achieving superior results through proper parameter tuning and operator selection.
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