Abstract-The problem with metaheuristics, includingTeaching-Learning-Based Optimization (TLBO) is that, it increases in the number of dimensions (D) leads to increase in the search space which increases the amount of time required to find an optimal solution (delay in convergence). Nowadays, multi-core systems are getting cheaper and more common. To solve the above large dimensionality problem, implementation of TLBO on a multi-core system using OpenMP API's with C/C++ is proposed in this paper. The functionality of a multicore system is exploited using OpenMP which maximizes the CPU (Central Processing Unit) utilization, which was not considered till now. The experimental results are compared with a sequential implementation of Simple TLBO (STLBO) with Parallel implementation of STLBO i.e. OpenMP TLBO, on the basis of total run time for standard benchmark problems by studying the effect of parameters, viz. population size, number of cores, dimension size, and problems of differing complexities. Linear speedup is observed by proposed OpenMP TLBO implementation over STLBO.
Genetic algorithms (GAs) are useful for solving multimodal problems. It is quite difficult to search the search space of the multimodal problem with large dimensions. There is a challenge to use all the core of the system. The Dual Population GA (DPGA) attempts to explore and exploit search space on the multimodal problems. Parallel GAs (PGAs) are better option to optimize multimodal problems. OpenMP GA is parallel version of GA. The Dual Population GA (DPGA) uses an extra population called reserve population to provide additional diversity to the main population through crossbreeding. DPGA and PGA, both provide niching technique to find optimal solution. Paper presents the experimentation of DPGA, OpenMP GA and SGA. The experimentation results show that the performance of the OpenMP GA is remarkably superior to that of the SGA in terms of execution time and speed up. OpenMP GA gives optimum solution in comparison with OpenMP GA and SGA for same parameter settings.
Abstract-Hybrid algorithm based on Particle SwarmOptimization (PSO) and Simulated annealing (SA) is proposed, to solve Flexible Job Shop Scheduling with five objectives to be minimized simultaneously: makespan, maximal machine workload, total workload, machine idle time & total tardiness. Rescheduling strategy used to shuffle workload once the machine breakdown takes place in proposed algorithm. The hybrid algorithm combines the high global search efficiency of PSO with the powerful ability to avoid being trapped in local minimum of SA. A hybrid multi-objective PSO (MPSO) and SA algorithm is proposed to identify an approximation of the pareto front for Flexible job shop scheduling (FJSSP). Pareto front and crowding distance is used for identify the fitness of particle. MPSO is significant to global search and SA used to local search. The proposed MPSO algorithm is experimentally applied on two benchmark data set. The result shows that the proposed algorithm is better in term quality of non-dominated solution compared to the other algorithms in the literature.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.