3 resultados para Multi-objective optimisation

em Cochin University of Science


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Assembly job shop scheduling problem (AJSP) is one of the most complicated combinatorial optimization problem that involves simultaneously scheduling the processing and assembly operations of complex structured products. The problem becomes even more complicated if a combination of two or more optimization criteria is considered. This thesis addresses an assembly job shop scheduling problem with multiple objectives. The objectives considered are to simultaneously minimizing makespan and total tardiness. In this thesis, two approaches viz., weighted approach and Pareto approach are used for solving the problem. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. Two metaheuristic techniques namely, genetic algorithm and tabu search are investigated in this thesis for solving the multiobjective assembly job shop scheduling problems. Three algorithms based on the two metaheuristic techniques for weighted approach and Pareto approach are proposed for the multi-objective assembly job shop scheduling problem (MOAJSP). A new pairing mechanism is developed for crossover operation in genetic algorithm which leads to improved solutions and faster convergence. The performances of the proposed algorithms are evaluated through a set of test problems and the results are reported. The results reveal that the proposed algorithms based on weighted approach are feasible and effective for solving MOAJSP instances according to the weight assigned to each objective criterion and the proposed algorithms based on Pareto approach are capable of producing a number of good Pareto optimal scheduling plans for MOAJSP instances.

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A Multi-Objective Antenna Placement Genetic Algorithm (MO-APGA) has been proposed for the synthesis of matched antenna arrays on complex platforms. The total number of antennas required, their position on the platform, location of loads, loading circuit parameters, decoupling and matching network topology, matching network parameters and feed network parameters are optimized simultaneously. The optimization goal was to provide a given minimum gain, specific gain discrimination between the main and back lobes and broadband performance. This algorithm is developed based on the non-dominated sorting genetic algorithm (NSGA-II) and Minimum Spanning Tree (MST) technique for producing diverse solutions when the number of objectives is increased beyond two. The proposed method is validated through the design of a wideband airborne SAR

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The objective of the study was to evaluate the survival response of multi-drug resistant enteropathogenic Escherichia coli and Salmonella paratyphi to the salinity fluctuations induced by a saltwater barrier constructed in Vembanadu lake, which separates the lake into a freshwater dominated southern and brackish water dominated northern part. Therefore, microcosms containing freshwater, brackish water and microcosms with different saline concentrations (5, 10, 15, 20, 25 ppt) inoculated with E. coli/S. paratyphi were monitored up to 34 days at 20 and 30 WC. E. coli and S. paratyphi exhibited significantly higher (p <0.05) survival at 20 WC compared to 30 WC in all microcosms. Despite fresh/brackish water, E. coli and S. paratyphi showed prolonged survival up to 34 days at both temperatures. They also demonstrated better survival potential at all tested saline concentrations except 25 ppt where a significantly higher (p<0.0001) decay was observed. Therefore, enhanced survival exhibited by the multi-drug resistant enteropathogenic E. coli and S. paratyphi over a wide range of salinity levels suggest that they are able to remain viable for a very long time at higher densities in all seasons of the year in Vembanadu lake irrespective of saline concentrations, and may pose potential public health risks during recreational activities