This paper deals with the utilizing of the Bayesian optimization algorithm (BOA) for the multiobjective optimization of combinatorial problems. Three probabilistic models used in the Estimation Distribution Algorithms (EDA), such as UMDA, BMDA and BOA which allow to search effectively on the promising areas of the combinatorial search space are discussed. The main attention is focused on the in...

Multiobjective 0/1 knapsack problems have been frequently used as test problems to examine the performance of evolutionary multiobjective optimization algorithms in the literature. It has been reported that their performance strongly depends on the choice of a constraint handling method. In this paper, we examine two implementation schemes of greedy repair: Lamarckian and Darwinian. In the Lama...

This paper presents a scheme for generating the Pareto front of multiobjective optimization problems by solving a sequence of constrained single-objective problems. Since the necessity of determining the constraint value a priori can be a serious drawback of the original epsilon-constraint method, our scheme generates appropriate constraint values adaptively during the run. A simple example pro...

This paper presents a new general-purpose algorithm for exact solving of combinatorial many-objective optimization problems. We call this new algorithm the guided improvement algorithm . The algorithm is implemented on top of the non-optimizing relational constraint solver Kodkod [24]. We compare the performance of this new algorithm against two algorithms from the literature (Gavanelli [11], L...

In this work, a new approach to selection in multiobjective evolutionary algorithms (MOEAs) is proposed. It is based on the portfolio selection problem, which is well known in financial management. The idea of optimizing a portfolio of investments according to both expected return and risk is transferred to evolutionary selection, and fitness assignment is reinterpreted as the allocation of cap...

A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a popu...

This paper deals with the utilizing of the Bayesian optimization algorithm (BOA) for the Pareto bi-criteria optimization of the 0/1 knapsack problem. The main attention is focused on the incorporation of the Pareto optimality concept into classical structure of the BOA algorithm. We have modified the standard algorithm BOA for one criterion optimization utilizing the known niching techniques to...

In the present work we are interested in the practical behavior of a new fptas to solve the approximation version of the 0-1 multi-objective knapsack problem. The proposed methodology makes use of very general techniques (such as dominance relations in dynamic programming) and thus may be applicable in the implementation of fptas for other problems as well. Extensive numerical experiments on va...

This paper proposes a decomposition-based approach to exactly solve the multi-objective Redundancy Allocation Problem for series-parallel systems. Redundancy allocation problem is a form of reliability optimization and has been the subject of many prior studies. The majority of these earlier studies treat redundancy allocation problem as a single objective problem maximizing the system reliabil...

In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multiobjective 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., nonParallel MOGAs) may fail to solve such intractable problem in a reasonab...