Effective Solutions and Algorithmic Approaches for Solving Combinatorial Optimization Problems Using Metaheuristic Techniques

Metaheuristics are renowned to present very efficient elucidation to many of today's combinatorial optimization problems in engineering, industrial, economical and scientific domains such as transportation, bioinformatics, logistics, business etc. Scheduling, timetabling, vehicle routing, resource allocation are intelligently and successfully tackled with Metaheuristic approaches such as Simulated Annealing, Tabu Search, Ant Colony Optimization, Harmony Search, Scatter Search, Iterated Local Search. Metaheuristics present itself as highly promising choice for nearly-optimal solutions in reasonable time where exact approaches are not applicable due to extremely large running times or other limitations. Meta-heuristic is a master strategy that guides and modifies other heuristics to produce solutions beyond those that are normally generated in a quest for local optimality. This paper highlights the various contemporary real life applications of Metaheuristics in the domain of industrial engineering and NP-hard problems.