Comparison of Nature Inspired Metaheuristic Algorithms

Metaheuristics is basically a higher level procedure, which generates a simpler procedure to solve an optimization problem. Optimization is the process of adjusting the inputs to or characteristics of a device, mathematical process, or experiment to find the minimum or maximum output or result. The input consists of variables; the process or function is known as the cost function, objective function, or fitness function; and the output is the cost or fitness. Since cost is something to be minimized, optimization becomes minimization. By searching over a large set of feasible solutions, metaheuristics can often find good solutions with less computational effort than algorithms, iterative methods, or simple heuristics. It is a refinement to the exhaustive search includes first searching a coarse sampling of the fitness function, then progressively narrowing the search to promising regions with a finer toothed comb. It speeds convergence and increases the number of variables that can be searched but also increases the odds of missing the global minimum. Metaheuristic algorithms are approximate and non-deterministic and are not problem specific. Metaheuristics are used for combinatorial optimization in which an optimal solution is sought over a discrete search-space, like the travelling salesman problem, where the search-space of candidate solutions grows faster than exponentially as the size of the problem increases, which makes an exhaustive search for the optimal solution infeasible. Many metaheuristic algorithms have inspiration coming from Nature. There are many such examples where the organisms (a population or a swarm) have optimized and adapted themselves to survive in this world. Some such algorithms are: Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Cuckoo Algorithms and many more. In this paper, the above mentioned algorithms are applied on Travelling Salesman Problem for comparison of their performance. The evaluation criteria is kept as the ”Time Taken to find the Optimum Solution”as benchmark 800 Kanika Malik and Akash Tayal