We have previously developed an adaptation of the simulated annealing for multi-objective combinatorial optimization (MOCO) problems to construct an approximation of the e$cient set of such problem. In order to deal with large-scale problems, we transform this approach to propose an interactive procedure. The method is tested on the multi-objective knapsack problem and the multi-objective assig...

In this paper, the researchers have proposed a multi-dimensional knapsack model for project capital budgeting problem in uncertain situation which has been modeled through fuzzy sets. The optimistic and pessimistic situations were considered and associated deterministic models were yielded. Numerical example has been supplied toillustrate the performance of proposed model. The results were prom...

In this paper, we present an approach, based on dynamic programming, for solving the 0-1 multi-objective knapsack problem. The main idea of the approach relies on the use of several complementary dominance relations to discard partial solutions that cannot lead to new non-dominated criterion vectors. This way, we obtain an efficient method that outperforms the existing methods both in terms of ...

Evolutionary algorithms are bio-inspired that can easily adapt to changing environments. Recent results in the area of runtime analysis have pointed out such as (1+1)~EA and Global SEMO efficiently reoptimize linear functions under a dynamic uniform constraint. Motivated by this study, we investigate single- multi-objective baseline evolutionary for classical knapsack problem where capacity var...

This paper deals with the bi-objective multi-dimensional knapsack problem. We propose the adaptation of the core concept that is effectively used in single objective multidimensional knapsack problems. The main idea of the core concept is based on the “divide and conquer” principle. Namely, instead of solving one problem with n variables we solve several sub-problems with a fraction of n variab...

In this thesis, we investigate the capabilities of Ant Colony Optimization (ACO) metaheuristic tosolve combinatorial and multi-objective optimization problems. First, we propose a taxonomy ofACO algorithms proposed in the literature to solve multi-objective problems. Then, we studydifferent pheromonal strategies for the case of mono-objective multidimensional knapsackproblem. We...

To further enhance the distribution uniformity and extensiveness of the solution sets and to ensure effective convergence of the solution sets to the Pareto front, we proposed a MOEA approach based on a clustering mechanism. We named this approach improved multi-objective evolutionary algorithm (LMOEA). This algorithm uses a clustering technology to compute and maintain the distribution and div...

it is shown in [1] that the optimal downlink radio resource allocation for non-realtime traffic in cellular cdma/tdma networks can be mapped to a multi-dimensional multiple-choice knapsack problem (mmkp) which is np-hard. in this correspondence we propose a heuristic algorithm with polynomial time complexity for this problem. numerical results indicate significant computational performance impr...

In this paper, we propose a hybrid fine-tuned multiobjective memetic algorithm hybridizing different solution fitness evaluation methods for global exploitation and exploration. To search across all regions in objective space, the algorithm uses a widely diversified set of weights at each generation, and employs a simulated annealing to optimize each utility function. For broader exploration, a...