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.
The ability to locate military units or equipment, police forces, and first responders optimally and to relocate idle units quickly in response to changing conditions is crucial to a country's ability to guard its critical facilities. Such facilities include vital components of the transportation infrastructure, government and monumental buildings, locations of large gatherings, emergency operations centers, and public and private utilities and communications facilities. In this paper, the problem of making optimal location and relocation decisions for a fixed fleet of response units in a transportation network, where travel conditions are uncertain, is addressed. A mixed integer linear program with multiple objectives (maximize secondary coverage and minimize cost) is presented. Because exact solution of such problems may require considerable computational effort, a metaheuristic based on the principles of genetic algorithms is proposed. The heuristic seeks the set of Pareto-optimal location and relocation decisions for each network state. All facilities of concern must be covered by at least one response unit. If the state of the network changes so that coverage is lost (e.g., travel times increase or a response unit is no longer available), one or more of the response units must be relocated. These relocation decisions are also addressed.
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