When solving problems, we need to find feasible solutions and make improvements to obtain the optimal global solution. However, due to limited resources and the complexity of most optimization problems, it is difficult to find very precise solutions. To deal with such problems, meta-heuristic optimization algorithms can be solved by providing approximate solutions. These algorithms help us find possible solutions in the search space by simulating biological, physical or social phenomena in nature. While these solutions may not be optimal, they are often close to optimal and perform well in practice. Therefore, meta-heuristic optimization algorithms have become a powerful tool for solving complex optimization problems.
Metaheuristic algorithms are widely used to solve various nonlinear and non-convex optimization problems. Especially in combinatorial optimization, traditional algorithms often have difficulty solving specific problems with uncertainty within a reasonable time. Metaheuristics can often find appropriate solutions with less computational effort than optimization algorithms, iterative methods, and simple greedy heuristics.
Metaheuristic algorithms play a key role in different fields. Many optimization problems are multi-objective functions with nonlinear constraints. For example, many engineering optimization problems are highly nonlinear and require solving multi-objective problems. In addition, artificial intelligence and machine learning problems often rely on large-scale data sets, and it is difficult to solve optimality by means of optimization problems. Therefore, metaheuristic algorithms play an important role in solving practical problems.
Metaheuristic algorithms are classified according to different modes of operation, including natural and unnatural heuristics, population-based and individual search, dynamic and static objective functions, different neighborhood structures, memory usage and memory-free methods wait.
1. Genetic algorithm (GA)
Genetic algorithm (GA) is a metaheuristic algorithm inspired by natural selection and the evolutionary process of natural genetics.
2. Simulated annealing (SA)
Simulated annealing (SA) is inspired by the heating and controlled cooling operations in metallurgy.
3. Tabu Search (TS)
Tabu Search (TS) is based on memory structures and uses local search methods to find potential solutions by checking their neighbors.
4. Swarm intelligence algorithm
The swarm intelligence algorithm is inspired by the social behavior of bird flocks, animal predation and hunting, bacterial growth and fish schools. Common ones include ant colony algorithm, particle swarm algorithm, bee colony algorithm, cuckoo search algorithm and so on.
5. Variable Neighborhood Search (VNS)
The Variable Neighborhood Search (VNS) algorithm explores initial solutions and improves them. Similar to tabu search, local search methods are applied iteratively and local optimal solutions are obtained from the solutions.
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