Abstract
In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multi-objective problems with non-convex and discrete Pareto front can take enormous computation time to converge to the true Pareto front. Hence, the classical multi-objective genetic algorithms (MOGAs) (i.e., non- Parallel MOGAs) may fail to solve such intractable problem in a reasonable amount of time. The proposed hybrid model will combine the best attribute of island and Jakobovic master slave models. We conduct an extensive experimental study in a multi-core system by varying the different size of processors and the result is compared with basic parallel model i.e., master-slave model which is used to parallelize NSGA-II. The experimental results confirm that the hybrid model is showing a clear edge over master-slave model in terms of processing time and approximation to the true Pareto front.
Highlights
Many of the real-world engineering optimization problems are multi-objective in nature, since they normally have several non-commensurable objectives that must be satisfied at the same time
The notion of optimum has to be redefined in this context and instead of aiming to find a single optimal solution; a procedure for solving multi-objective optimization problems (MOP) should determine a set of good compromises or trade-off solutions, generally known as Pareto optimal solutions, where the decision maker will get enough flexibility to choose a particular solution
The problem is solved by proposed hybrid model and the result is compared with the master-slave model
Summary
Many of the real-world engineering optimization problems are multi-objective in nature, since they normally have several non-commensurable objectives that must be satisfied at the same time. The notion of optimum has to be redefined in this context and instead of aiming to find a single optimal solution; a procedure for solving MOP should determine a set of good compromises or trade-off solutions, generally known as Pareto optimal solutions, where the decision maker will get enough flexibility to choose a particular solution. These solutions are optimal in the wider sense that no other solution in the search space is superior when all objectives are considered. Evolutionary algorithms (EAs) have the potential for finding multiple Pareto optimal solutions in a single run and have been widely used in this area
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