Student-t processes have recently been proposed as an appealing alternative nonparameteric function prior. They feature enhanced flexibility and predictive variance. In this work the use of Student-t processes are explored for multiobjective Bayesian optimization. In particular, an analytical expression for the hypervolume-based probability of improvement is developed for independent Student-t ...

In this paper, we propose a sufficient condition for a solution to be optimal for a 2-additive Choquet integral in the context of multiobjective combinatorial optimization problems. A 2-additive Choquet optimal solution is a solution that optimizes at least one set of parameters of the 2-additive Choquet integral. We also present a method to generate 2-additive Choquet optimal solutions of mult...

We propose a new perspective for solving systems of nonlinear equations by viewing them as a multiobjective optimization problem where every equation represents an objective function whose goal is to minimize the difference between the rightand left-hand side of the corresponding equation of the system. An evolutionary computation technique is suggested to solve the problem obtained by transfor...

An indicator-based evolutionary multiobjective optimization algorithm (EMOA) is introduced which incorporates the contribution to the unary R2-indicator as the secondary selection criterion. First experiments indicate that the R2-EMOA accurately approximates the Pareto front of the considered continuous multiobjective optimization problems. Furthermore, decision makers’ preferences can be inclu...

In this paper, tabu programming for solving multiobjective optimization problems has been considered. Tabu search algorithm has been extended by using a computer program instead of a mathematical variable. For finding Paretooptimal solutions, the ranking procedure in the neighborhood of the current solutions has been applied. Moreover, the multiobjective optimization problem of task assignment ...

Very often real world applications have several multiple conflicting objectives. Recently there has been a growing interest in evolutionary multiobjective optimization algorithms which combines two major disciplines: evolutionary computation and the theoretical frameworks of multicriteria decision making. In this introductory chapter, we define some fundemental concepts of multiobjective optimi...

Harmony Search metaheuristic is successfully used in several applications of science and engineering. However, its effectiveness in solving multiobjective optimization problems using the concepts of Pareto optimality, remains unproved. This paper presents two proposals of the Harmony Search metaheuristic for multiobjective optimization, using the ZDT functions as a test bed. Performance metrics...

Maximin strategy has its origin in game theory, but it can be adopted for effective multiobjective optimization. This paper proposes a particle swarm multiobjective optimiser, maximinPSO, which uses a fitness function derived from the maximin strategy to determine Pareto-domination. The maximin fitness function has some very desirable properties with regard to multiobjective optimization. One a...

In this paper, we focus on expensive multiobjective optimization problems and propose a method to predict an approximation of the Pareto optimal set using classification of sampled decision vectors as dominated or nondominated. The performance of our method, called EPIC, is demonstrated on a set of benchmark problems used in the multiobjective optimization literature and compared with state-of ...

This tutorial will review some of the basic concepts related to evolutionary multiobjective optimization (i.e., the use of evolutionary algorithms to handle more than one objective function at a time). The most commonly used evolutionary multiobjective optimization techniques will be described and criticized, including some of their applications. Theory, test functions and metrics will be also ...