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Metaheuristics

Generally speaking heuristics aim to find a good enough solution to a problem in a reasonable amount of time. These are mostly based on experiences and common sense. Metaheutistics iteratively tries to improve a candidate solution with respect to a given measure of quality.They rarely make assumptions about the problem being optimized They can search very large spaces of candidate solutions. Whereas they do not guarantee the finding of an optimal solution. Some of the popularly used metaheuristics for optimization problems are: • Simulated Annealing (SA), • Particle Swarm Optimization (PSO), • Differential Evolution (DE), • Genetic Algorithm (GA), • Evolutionary Strategy (ES).
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