The relationship between bilevel optimization and multiobjective optimization has been studied by several authors and there have been repeated attempts to establish a link between the two. We unify the results from the literature and generalize them for bilevel multiobjective optimization. We formulate sufficient conditions for an arbitrary binary relation to guarantee equality between the effi...

This paper presents the methodology used for finding the optimal set of foot trajectories for a quadruped robot using manual tuning and multiobjective genetic algorithm optimization that provide energy efficient and fast locomotion. Manual trajectory tuning is used to obtain initial set of trajectories for the multiobjective GA optimization. The multiobjective optimization evaluates the energy ...

abstract this paper presents a multiobjective power control algorithm that updates the transmitted power based on local information. the proposed algorithm is expanded by using multiobjective optimization schemes. the objectives to be optimized in this paper are determined so as to reduce the sinr fluctuations as well as maintaining the sinr to an acceptable level with minimizing an average tra...

This report documents the program and outcomes of the Dagstuhl Seminar 15031 Understanding Complexity in Multiobjective Optimization. This seminar carried on the series of four previous Dagstuhl Seminars (04461, 06501, 09041 and 12041) that were focused on Multiobjective Optimization, and strengthening the links between the Evolutionary Multiobjective Optimization (EMO) and Multiple Criteria De...

The Genetic Immune Strategy for Multiple Objective Optimization (GISMOO) is a hybrid algorithm for solving multiobjective problems. The performance of this approach has been assessed using a classical combinatorial multiobjective optimization benchmark: the multiobjective 0/1 knapsack problem (MOKP) [1] and two-dimensional unconstrained multiobjective problems (ZDT) [2]. This paper shows that t...

In this paper, we present the first proposal to use a cultural algorithm to solve multiobjective optimization problems. Our proposal uses evolutionary programming, Pareto ranking and elitism (i.e., an external population). The approach proposed is validated using several examples taken from the specialized literature. Our results are compared with respect to the NSGA-II, which is an algorithm r...

Differential Evolution (DE) is a simple but powerful evolutionary optimization algorithm with many successful applications. In this paper we propose Differential Evolution for Multiobjective Optimization (DEMO) – a new approach to multiobjective optimization based on DE. DEMO combines the advantages of DE with the mechanisms of Paretobased ranking and crowding distance sorting, used by state-of...

Here, a quasi-Newton algorithm for constrained multiobjective optimization is proposed. Under suitable assumptions, global convergence of the algorithm is established.

Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. As evolutionary algorithms possess several characteristics that are desirable for this type of problem, this class of search strategies has been used for multiobjective optimization for more than a decade. Meanwhile evolutionary multiobjective optimization has become established as a separate subdi...