Cuckoo search is a swarm-intelligence-based algorithm that is very effective for solving highly nonlinear optimization problems. In this paper, the multiobjective cuckoo search is extended so as to obtain high-quality Pareto fronts more accurately for multiobjective optimization problems with complex constraints. The proposed approach uses a combination of the cuckoo search with non-dominated sorting and archiving techniques. The performance of the proposed approach is validated by seven test problems. The convergence property and diversity as well as uniformity are compared with those of the NSGA-II. The results show that the proposed approach can find Pareto fronts with better uniformity and quicker convergence.
In this paper we present an approach to designing swarms of autonomous, adaptive robots. An observer/controller framework that has been developed as part of the Organic Computing initiative provides the architectural foundation for the individuals' adaptivity. Relying on an extended Learning Classifier System (XCS) in combination with adequate simulation techniques, it empowers the individuals to improve their collaborative performance and to adapt to changing goals and changing conditions. We elaborate on the conceptual details, and we provide first results addressing different aspects of our multi-layered approach. Not only for the sake of generalisability, but also because of its enormous transformative potential, we stage our research design in the domain of quad-copter swarms that organise to collaboratively fulfil spatial tasks such as maintenance of building facades. Our elaborations detail the architectural concept, provide examples of individual self-optimisation as well as of the optimisation of collaborative efforts, and we show how the user can control the swarm at multiple levels of abstraction. We conclude with a summary of our approach and an outlook on possible future steps.
In this paper, we have developed an efficient hybrid data mining approach. The proposed data mining approach called gSSO is a modification introduced to simplified swarm optimization and based on K-harmonic means (KHM) algorithm to help the KHM algorithm escape from local optimum. To test its solution quality, the proposed gSSO is compared with other recently introduced KHM-based Algorithms in iris dataset in the UCI database. The experimental results conclude that the proposed gSSO outperforms other algorithms in the solution quality of all aspects (AVG, MIN, MAX, and STDEV) in space and stability.
In view of the existing polygonal approximation algorithm of digital curves can't effectively solve the problem of polygonal approximation constrained by the offset direction, this paper proposes an algorithm of polygonal approximation constrained by the offset direction. First, the offset polygon of the original digital curve is calculated under the control of offset direction and distance. Second, the summation of the squared Euclidean distances between the vertices on the offset polygon and its corresponding segment in the approximated polygon is selected as the fitness function. Finally, under the control of the offset distance and fitness function, this paper implements a PSO-based polygonal approximation algorithm to approximate the offset polygon. Experiments show that the proposed method can not only satisfy the polygonal approximation with directional requirements, but also can greatly improve the operating efficiency.
Particle swarm optimization (PSO) algorithms have a number of parameters to which their behaviour is sensitive. In order to avoid problem-specific parameter tuning, a number of self-adaptive PSO algorithms have been proposed over the past few years. This paper compares the behaviour and performance of a selection of self-adaptive PSO algorithms to that of time-variant algorithms on a suite of 22 boundary constrained benchmark functions of varying complexities. It was found that only two of the nine selected self-adaptive PSO algorithms performed comparably to similar time-variant PSO algorithms. Possible reasons for the poor behaviour of the other algorithms as well as an analysis of the more successful algorithms is performed in this paper.
