Improving the performance of evolutionary algorithms for the multiobjective 0/1 knapsack problem using ϵ -dominance
نویسنده
چکیده
The 0/1 knapsack problem is a well known problem occurring in many real world problems. The problem is NP-Complete. The multiobjective 0/1 knapsack problem is a generalization of the 0/1 knapsack problem in which multiple knapsacks are considered. A new evolutionary algorithm for solving multiobjective 0/1 knapsack problem is proposed in this paper. This algorithm used a ε-dominance relation for direct comparison of two solutions. Several numerical experiments are performed using the best recent algorithms proposed for this problem. Experimental results clearly show that the proposed algorithm outperforms the existing evolutionary approaches for this problem.
منابع مشابه
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Evolutionary algorithms (EA’s) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly...
متن کاملZitzler and Thiele : Multiobjective Evolutionary Algorithms | Comparison and Strength Pareto Approach
| Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often connicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of diierent methods presented up to now remain mostly ...
متن کاملMultiobjective Prototype Optimization with Evolved Improvement Steps
Recently, a new iterative optimization framework utilizing an evolutionary algorithm called ”Prototype Optimization with Evolved iMprovement Steps” (POEMS) was introduced, which showed good performance on hard optimization problems large instances of TSP and real-valued optimization problems. Especially, on discrete optimization problems such as the TSP the algorithm exhibited much better searc...
متن کاملMultiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
Since 1985 various evolutionary approaches to multiobjec-tive optimization have been developed, capable of searching for multiple solutions concurrently in a single run. But the few comparative studies of diierent methods available to date are mostly qualitative and restricted to two approaches. In this paper an extensive, quantitative comparison is presented, applying four multiobjective evolu...
متن کاملUsing ACO in MOEA/D for Multiobjective Combinatorial Optimization
Combining ant colony optimization (ACO) and multiobjective evolutionary algorithm based on decomposition (MOEA/D), this paper proposes a multiobjective evolutionary algorithm, MOEA/D-ACO. Following other MOEA/D-like algorithms, MOEA/D-ACO decomposes an multiobjective optimization problem into a number of single objective optimization problems. Each ant (i.e. agent) is responsible for solving on...
متن کامل