We introduce a novel approximation method for multiobjective optimization problems called PAINT–SiCon. The method can construct consistent parametric representations of Pareto sets, especially for nonconvex problems, by interpolating between nondominated solutions of a given sampling both in the decision and objective space. The proposed method is especially advantageous in computationally expe...

We consider biobjective shortest path problems in networks with fuzzy arc lengths. Considering the available studies for single objective shortest path problems in fuzzy networks, using a distance function for comparison of fuzzy numbers, we propose three approaches for solving the biobjective prob- lems. The rst and second approaches are extensions of the labeling method to solve the sing...

In trying to solve multiobjective optimization problems, many traditional methods scalar-ize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about the underlying problem. Moreover, in solving multiobjective problems, designers may be interested in...

The resource allocation problem is a fundamental problem in Grid and Cloud computing environments. This paper focuses on constructing nondominated (ND) local coteries to solve the problem in a distributed way. Distributed algorithms using coteries usually incur low communication overhead and have high degree of fault-tolerance, and ND coteries are candidates for the algorithms to achieve the hi...

The shortest path routing problem is a multiobjective nonlinear optimization problem with constraints. This problem has been addressed by considering Quality of service parameters, delay and cost objectives separately or as a weighted sum of both objectives. Multiobjective evolutionary algorithms can find multiple pareto-optimal solutions in one single run and this ability makes them attractive...

The Vector Evaluated Particle Swarm Optimisation algorithm is widely used to solve multiobjective optimisation problems. This algorithm optimises one objective using a swarm of particles where their movements are guided by the best solution found by another swarm. However, the best solution of a swarm is only updated when a newly generated solution has better fitness than the best solution at t...

A coterie under an underlying set U is a family of subsets of U such that every pair of subsets has at least one element in common, but neither is a subset of the other. A coterie C under U is said to be nondominated (ND) if there is no other coterie D under U such that, for every Q ∈ C, there exists Q ∈ D satisfying Q ⊆ Q. We introduce the operation σ which transforms a ND coterie to another N...

The optimality conditions for linear programming problems with fuzzy coefficients are derived in this paper. Two solution concepts are proposed by considering the orderings on the set of all fuzzy numbers. The solution concepts proposed in this paper will follow from the similar solution concept, called the nondominated solution, in the multiobjective programming problem. Under these settings, ...

To interact effectively, agents must enter into commitments. What should an agent do when these commitments conflict? We describe Coco, an approach for reasoning about which specific commitments apply to specific parties in light of general types of commitments, specific circumstances, and dominance relations among specific commitments. Coco adapts answer-set programming to identify a maximal s...