In this paper, an improved multi-objective differential evolution algorithm(IDEA) is proposed for multi-objective optimization problems. In IDEA, the select operator combines the advantages of DE with the mechanisms of Pareto-based ranking and distance density, besides, a randomly migration strategy is proposed. IDEA is implemented on four classical multi-objective problems, the simulation resu...

In this paper, we propose a framework using local models for multi-objective optimization to guide the search heuristic in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres in the decision space. These spheres are usually guided, using some direction information, in the decision space towards the areas with non-dominated solutions. We u...

Nearly all Multi-Objective Evolutionary Algorithms (MOEA) rely on random generation of initial population. In large and complex search spaces, this random method often leads to an initial population composed of infeasible solutions only. Hence, the task of a MOEA is not only to converge towards the Pareto-optimal front but also to guide the search towards the feasible region. This paper propose...

Complex distribution networks are pervasive in biology. Examples include nutrient transport in the slime mold Physarum polycephalum as well as mammalian and plant venation. Adaptive rules are believed to guide development of these networks and lead to a reticulate, hierarchically nested topology that is both efficient and resilient against perturbations. However, as of yet no mechanism is known...

Research into solving multiobjective optimization problems (MOP) has as one of its an overall goals that of developing and defining foundations of an Evolutionary Computation (EC)-based MOP theory. In this paper, we introduce relevant MOP concepts, and the notion of Pareto optimality, in particular. Specific notation is defined and theorems are presented ensuring Paretobased Evolutionary Algori...

The practical use of multiobjective optimization tools in industry is still an open issue. A strategy for reduction of objective function calls is often essential, at a fixed degree of Pareto Optimal Front (POF) approximation accuracy . To this aim an extension of single-objective NN-based GRS methods to Pareto Optimal Front (POF) approximation is proposed. Such an extension is not at all strai...

Evolutionary multi-objective algorithms are widely considered to have two goals: convergence towards the true pareto front and maintaining a diverse set of solutions. Here we are primarily concerned with the first goal of convergence, in particular when one or more variables must converge to a common value. Using a well-known test suite, we discuss the difficulties that are currently impeding c...

We propose a multiobjective strategy for model predictive control (MPC) that we term utopiatracking MPC. The controller minimizes, in some norm, the distance of its cost vector to that of the unreachable steady-state utopia point. Stability is ensured by using a terminal constraint to a selected point along the steady-state Pareto front. One of the key advantages of this approach is that multip...

In some real applications of Statistical Process Control it is necessary to design a control chart to not detect small process shifts, but keeping a good performance to detect moderate and large shifts in the quality. In this work we develop a new quality control chart, the synthetic T control chart, which can be designed to cope with this objective. A multi-objective optimization is carried ou...

NM-landscapes have been recently introduced as a class of tunable rugged models. They are a subset of the general interaction models where all the interactions are of order less or equal M . The Boltzmann distribution has been extensively applied in single-objective evolutionary algorithms to implement selection and study the theoretical properties of model-building algorithms. In this paper we...