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Ph.D Research Guidance in Metaheuristic Computing

In recent years, the metaheuristic model has emerged as one of the potential solutions for optimization problems. The term ‘Metaheuristics’ is the combination of the terms ‘Meta’ and ‘Heuristic’, referring to the discovering of the solution by trial and error at a higher level. In particular, ‘Meta’ - higher level and ‘Heuristics’ - trial and error based goal finding. Metaheuristic algorithm or computing ensures resolving the difficult optimization problems through the local search and global search within a reasonable time. Metaheuristic algorithms are broadly classified into single solutions and population-based algorithms. In metaheuristic computing, single solution-based algorithms, including the local and neighborhood searching algorithms and population-based algorithms, including the evolutionary and swarm intelligence algorithms. Tabu search, simulated annealing, and local search are several examples of single solution-based metaheuristic algorithms. Differential Evolution, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) are several examples of population-based metaheuristic algorithms. Metaheuristic algorithms have been widely used to handle large-scale problems to generate, search, select, or find a good solution. It has been widely applied in the engineering, transportation, social science, and business fields to optimize computation.

Courses

Ph.D

Computer Science and Engineering, Computer Science, Computer applications, Information Technology and Computer Networks

M.E/M.Tech/M.S

Computer Science and Engineering, Computer Science, Computer Networks and Information Technology