We consider the problem of finding the Pareto front of the expected deployment cost of wireless caches in the plane and the expected retrieval cost of a client requesting data from the caches. The data is allocated at the caches according to partitioning and coding strategies. We show that under coding, it is optimal to deploy many caches with low storage capacity. For partitioning, we derive a...

Previous work on multiobjective genetic algorithms has been focused on preventing genetic drift and the issue of convergence has been given little attention. In this paper, we present a simple steady-state strategy, Pareto Converging Genetic Algorithm (PCGA), which naturally samples the solution space and ensures population advancement towards the Pareto-front. PCGA eliminates the need for shar...

This paper presents a new method that effectively determines a Pareto front for biobjective optimization with potential application to multiple objectives. A traditional method for multiobjective optimization is the weighted sum method, which seeks Pareto optimal solutions one by one by systematically changing the weights among the objective functions. Previous research has shown that this meth...

This paper presents a new method that effectively determines a Pareto front for bi-objective optimization with potential application to multiple objectives. A traditional method for multiobjective optimization is the weighted-sum method, which seeks Pareto optimal solutions one by one by systematically changing the weights among the objective functions. Previous research has shown that this met...

We develop a generic tool for approximating the Pareto front of multicriteria optimization problems using stochastic local search algorithms. Our algorithmic scheme handles problems that the multi-criteria context introduces into the local search framework such as the non-uniqueness of the best neighbor and the potentially large size of the Pareto front. We demonstrate the performance of our al...

Evolutionary multiobjective optimization usually attempts to find a good approximation to the complete Pareto optimal front. However, often the user has at least a vague idea about what kind of solutions might be preferred. If such information is available, it can be used to focus the search, yielding a more fine-grained approximation of the most relevant (from a user’s perspective) areas of th...

We present a decision-making procedure, for a problem where no solution is known a priori. The decision-making procedure is a human powered genetic algorithm that uses human beings to produce variations and evaluate the partial solution proposed. Following [1] we then select the pareto front of the proposed partial solutions, eliminating the dominated ones. We then feed the results back to the ...

This paper presents a new strategy that can solve the Grid task scheduling problem with multiple objectives (NP-Hard) in polynomial time using evolutionary algorithms. The results obtained by our proposed algorithm were compared and evaluated against the -constraints classic Multi-Objective Optimization method, which uses the deterministic algorithm of Branch and Bound to find the real Pareto f...

In this paper we study the Pareto-optimal solutions in convex multi-objective optimization with compact and convex feasible domain. One of the most important problems in multi-objective optimization is the investigation of the topological structure of the Pareto sets. We present the problem of construction of a retraction function of the feasible domain onto Paretooptimal set, if the objective ...

We propose a multi-objective evolutionary algorithm (MOEA), named the Hyper-volume Evolutionary Algorithm (HVEA). The algorithm is characterised by three components. First, individual fitness evaluation depends on the current Pareto front, specifically on the ratio of its dominated hyper-volume to the current Pareto front hyper-volume, hence giving an indication of how close the individual is t...