Ameliorating Metaheuristic in Optimization Domains

Metaheuristic algorithms, such as genetic algorithms and simulated annealing, are search techniques that are inspired by nature. They aim to avoid a problem encountered by traditional search techniques such as hill climbing - the danger of getting stuck at a local optimum. Many achieve this by adding a stochastic element, such as the ability to accept a move from a candidate solution to one that appears worse. Metaheuristic algorithms have been applied to a wide range of optimization. Solutions to project management problems can be managed by applying search techniques. This paper aims to explore if metaheuristic can be applied in project management domain to get optimal results.