Unknown
Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.
The traveling salesman problem (TSP) is one of the most studied problems in computational intelligence and operations research. Since its first formulation, a myriad of works has been published proposing different alternatives for its solution. Additionally, a plethora of advanced formulations have also been proposed by the related practitioners, trying to enhance the applicability of the basic TSP. This chapter is firstly devoted to providing an informed overview on the TSP. For this reason, we first review the recent history of this research area, placing emphasis on milestone studies contributed in recent years. Next, we aim at making a step forward in the field proposing an experimentation hybridizing three different reputed bio-inspired computational metaheuristics (namely, particle swarm optimization, the firefly algorithm, and the bat algorithm) and the novelty search mechanism. For assessing the quality of the implemented methods, 15 different datasets taken from the well-known TSPLIB have been used. We end this chapter by sharing our envisioned status of the field, for which we identify opportunities and challenges which should stimulate research efforts in years to come.
No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
No abstract is provided for this article.
In real-world engineering problems, several conflicting objective functions have often to be optimized simultaneously. Typically, the objective functions of these problems are too complex to solve using derivative-based optimization methods. Integration of navigation and radar functionality with communication applications is such a problem. Designing sequences for these systems is a difficult task. This task is further complicated by the following factors: (i) conflicting requirements on autocorrelation and crosscorrelation characteristics; (ii) the associated cost functions might be irregular and may have several local minima. Traditional or gradient based optimization methods may face challenges or are unsuitable to solve such a complex problem. In this paper, we pose simultaneous optimization of autocorrelation and crosscorrelation characteristics of Oppermann sequences as a multiobjective problem. We compare the performance of prominent state-of-the-art multiobjective evolutionary meta-heuristic algorithms to design Oppermann sequences for integrated radar and communication systems.
This chapter introduces a powerful tool for classification and regression, including linear support vector machines, nonlinear support vector machines, and support vector regression.
We study a situation where a swarm of robots is deployed to handle multiple different targets in a confined unknown area. The targets are found in real time and each target requires a certain amount of resources. An individual robot may not have sufficient capabilities for its execution, therefore an announcement process can start. We address the issue of how each robot responds itself to one of the discovered targets in an efficient way, considering a dynamic scenario where failure of the robots and unreliable communications unpredictably can occur. We propose a network architecture that incorporates a self-regulating mechanism allowing the distribution among the targets, with the minimal exchange of information. We have conducted experiments for evaluating our proposed approach in a simulated environment, considering different network parameters and studying the scalability and the robustness of the proposed model.
No abstract is provided for this article.
Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
No abstract is provided for this article.
New results are derived for the stability analysis of bilinear systems (BLS) with output feedback of a general form which includes linear control functions, quadratic functions, all functions which satisfy Lipschitz condition and others. Theorems and applications are obtained for time-variant, continuous and discrete BLS. The results form a base for adaptive trajectory-tracking controllers. The sufficient conditions which are derived can be applied directly to the range of admissible controller gains for given plant parameters, or there is a trade off between controller and plant design parameters. The method does not depend on the computation of a Liapunov function and indirect stability analysis as do most techniques which are available in the literature. In the continuous case, certain necessary conditions also are derived. A field-controlled, electric motor example is presented.
