In this paper, multiobjective synchronization of chaotic systems is investigated by especially simultaneously minimizing optimization of control cost and convergence speed. The coupling form and coupling strength are optimized by an improved multiobjective evolutionary approach that includes a hybrid chromosome representation. The hybrid encoding scheme combines binary representation with real ...

Multiobjective combinatorial optimization problems are known to be hard problems for two reasons: their decision versions are often NPcomplete and they are often intractable. Apart from this general observation, are there also variants or cases of multiobjective combinatorial optimization problems which are easy and, if so, what causes them to be easy? This article is a first attempt to provide...

Multiobjective reinforcement learning algorithms extend reinforcement learning techniques to problems with multiple conflicting objectives. This paper discusses the advantages gained from applying stochastic policies to multiobjective tasks and examines a particular form of stochastic policy known as a mixture policy. Two methods are proposed for deriving mixture policies for episodic multiobje...

This study has developed and evaluated a multiobjective metaheuristic for redistricting to draw territory lines for geographical zones for the purpose of space control. The proposed multiobjective metaheuristic is briefly explained to shows its components and functionality. The redistricting problem definition is discussed, followed by the metaheuristic and the multiobjective decision rules. Th...

In this paper, we present a novel approach for codebook design using multiobjective genetic optimization. We partition the pattern space into perceptive classes, which are then clustered into hyperspheres based on multiobjective criteria. Multiobjective genetic algorithms perform optimisation on a vector space of objectives and are able to explore the search space for a set of equally viable an...

In recent years, some researchers considered image color quantization as a single-objective problem and applied heuristic algorithms to solve it. This paper establishes a multiobjective image color quantization model with intracluster distance and intercluster separation as its objectives. Inspired by a multipopulation idea, a multiobjective image color quantization algorithm based on self-adap...

In practical applications it is common to have several conflicting objective functions to optimize. Frequently, these functions are nondifferentiable or discontinuous, could be subject to numerical noise and/or be of black-box type, preventing the use of derivative-based techniques. In this paper we give an overview of some recent developments in Derivative-free Multiobjective Optimization. We ...

This paper deals with multiobjective optimization techniques for a class of hybrid optimal control problems in mechanical systems. We deal with general nonlinear hybrid control systems described by boundary-value problems associated with hybrid-type Euler-Lagrange or Hamilton equations. The variational structure of the corresponding solutions makes it possible to reduce the original “mechanical...

This chapter describes various approaches to the use of evolutionary algorithms and other metaheuristics in interactive multiobjective optimization. We distinguish the traditional approach to interactive analysis with the use of single objective metaheuristics, the semi-a posteriori approach with interactive selection from a set of solutions generated by a multiobjective metaheuristic, and spec...

A new local search algorithm for multiobjective optimization problems is proposed to find the global optima accurately and diversely. This paper models the cooperatively local search as a potential field, which is called neighborhood field model (NFM). Using NFM, a new Multiobjective Neighborhood Field Optimization (MONFO) algorithm is proposed. In MONFO, the neighborhood field can drive each i...