A Novel Evolutionary Algorithm for Multidimensional Knapsack Problem

Binary optimization problems are in the most case the NP-hard problems that call to satisfy an objective function with or without constraints. Various optimization problems can be formulated in binary expression whither they can be resolved in easier way. Optimization literature supplies a large number of approaches to find solutions to binary hard problems. However, most population-based algorithms have a great tendency to be trapped in local optima particularly when solving complex optimization problems. In this paper, the authors introduce a new efficient population-based technique for binary optimization problems (that we called EABOP). The proposed algorithm can provide an effective search through a new proposed binary mutation operator. The performance of our approach was tested on hard instances of the multidimensional knapsack problem. The obtained results show that the new algorithm is able of quickly obtaining high-quality solutions for most hard instances of the problem. A Novel Evolutionary Algorithm for Multidimensional Knapsack Problem