Particle swarm optimization (PSO) is a stochastic, population-based optimization method, which has been applied successfully to a wide range of problems. However, PSO is computationally expensive and suffers from premature convergence. In this paper, opposition-based learning is used to improve the performance of PSO. The performance of the proposed approaches is investigated and compared with PSO when applied to eight benchmark functions. The experiments conducted show that opposition-based learning improves the performance of PSO.

A clustering method that is based on barebones particle swarm (BB) is developed in this paper. BB is a variant of particle swarm optimization (PSO) where parameter tuning is not required. The proposed algorithm finds the centroids of a user specified number of clusters, where each cluster groups together similar patterns. The application of the proposed clustering algorithm to the problem of unsupervised classification and segmentation of images is investigated. To illustrate its wide applicability, the proposed algorithms are then applied to synthetic, MRI and satellite images. Experimental results show that the BB-based clustering algorithm performs very well compared to other state-of-the-art clustering algorithms in all measured criteria.

This paper presents a hybrid evolutionary algorithm for constrained multiple destinations routing problem. The problem can be formulated as minimising tree cost under several constraints or QoS metrics. Computing such constrained multicast tree has been proven to be NP-complete. The proposed hybrid algorithm is based on a population based incremental learning algorithm and a constrained distance network heuristic (or CKMB) algorithm. In the proposed algorithm, CKMB is utilised as a decoding scheme. Experimental results show that, in most cases, the proposed algorithm yields better solutions than other heuristic algorithms proposed in the literature including the best known one BSMA.

This paper tackles the issue of bandwidth allocation in asynchronous transfer mode (ATM) networks using recently developed tools of computational intelligence. The efficient bandwidth allocation technique implies effective resources utilization of the network. The fluid flow model has been used effectively among other conventional techniques to estimate the bandwidth for a set of connections. However, such methods have been proven to be inefficient at times in coping with varying and conflicting bandwidth requirements of the different services in ATM networks. This inefficiency is due to the computational complexity of the model. To overcome this difficulty, many approximation-based solutions, such as the fluid flow approximation technique, were introduced. Although such solutions are simple, in terms of computational complexity, they nevertheless suffer from potential inaccuracies in estimating the required bandwidth. Soft computing-based bandwidth controllers, such as neural networks- and neurofuzzy-based controllers, have been shown to effectively solve an indeterminate nonlinear input-output (I-O) relations by learning from examples. Applying these techniques to the bandwidth allocation problem in ATM network yields a flexible control mechanism that offers a fundamental tradeoff for the accuracy-simplicity dilemma.

In this paper, a neuro-fuzzy based classification technique is adopted to efficiently deal with the classification problem of the heterogeneous medical data sets. The Proposed classification technique is based on the neuro-fuzzy classification (NE-FCLASS) system. However, several improvements are introduced to the origin NEFCLASS. The motivation of this work is triggered by the fact that most conventional classification techniques are capable of handling the numeric data sets but not the heterogenous ones. This can be seen in the classification of the medical data sets. This paper tackles the data classification problem of two medical diseases. The first data set, which is a numeric data set, is related to the Wisconsin breast cancer diagnosis. The second is a heterogeneous data set and is the Wisconsin heart disease diagnosis. Experimental results demonstrate that the proposed technique can effectively improve the classification performance of heterogenous data sets.

We examine in this article the complex problem of simultaneous position and internal force control in multiple cooperative manipulator systems. This is done in the presence of unwanted parametric and modeling uncertainties as well as external disturbances. A decentralized adaptive hybrid intelligent control scheme is proposed here. The controller makes use of a multi-input multi-output fuzzy logic engine and a systematic online adaptation mechanism. Unlike conventional adaptive controllers, the proposed one does not require a precise model of the system's dynamics. The performance of the proposed controller is compared to that of a well known conventional adaptive controller.

We present soft computing-based results pertaining to the hierarchical tuning process of PID controllers located within the control loop of a class of nonlinear systems. The results are compared with PID controllers implemented either in a stand alone scheme or as a part of conventional gain scheduling structure. This work is motivated by the increasing need in the industry to design highly reliable and efficient controllers for dealing with regulation and tracking capabilities of complex processes characterized by nonlinearities and possibly time varying parameters. The soft computing-based controllers proposed are hybrid in nature in that they integrate within a well-defined hierarchical structure the benefits of hard algorithmic controllers with those having supervisory capabilities. The controllers proposed also have the distinct features of learning and auto-tuning without the need for tedious and computationally extensive online systems identification schemes.

This paper presents a survey about different types of fuzzy information measures. A number of schemes have been proposed to combine the fuzzy set theory and its application to the entropy concept as a fuzzy information measurements. The entropy concept, as a relative degree of randomness, has been utilized to measure the fuzziness in a fuzzy set or system. However, a major difference exists between the classical Shannon entropy and the fuzzy entropy. In fact while the later deals with vagueness and ambiguous uncertainties, the former tackles probabilistic uncertainties (randomness).

This paper presents the application of soft computing-based techniques to the bandwidth allocation (BA) problem in ATM networks. Efficient bandwidth allocation technique implies effective resources utilization. The fluid flow model has been known to be among the most accurate conventional methods to estimate the bandwidth of a set of connections. However, and due to the computational complexity, such methods have been proven to be inefficient in coping with varying and conflicting bandwidth requirements in ATM networks. To overcome this difficulty, many approximation-based solutions were introduced. Although such solutions are not simple, they nevertheless suffer from possible inaccuracy in estimating the required bandwidth. Soft computing-based bandwidth controllers, such as neural networks and neurofuzzy based controllers, have the capability to solve indeterminate non-linear input-output relations by learning from examples. Applying these techniques to the bandwidth allocation problem in ATM network yields a flexible control mechanism that offers a fundamental trade-off for the accuracy-simplicity dilemma.

We discuss here the implementation aspects of recently developed tools of computational intelligence for tackling the issue of joint trajectory generation of a class of multi-joint cooperative robotic systems. This is closely related to the inverse kinematics problem which usually represents a heavy computational burden on the processing power of any complex robotic structure. High nonlinearities, heavy coupling between the degrees of freedom, and time variant configuration of the robot structure heavily contribute to these difficulties. Soft computing techniques have surged in recent years as effective computational tools for emulating the human capabilities when dealing with complex systems. Some of them are used here to synthesize approaches capable of substantially improving solving the inverse kinematics problem for a class of robotic systems and help generating the joint trajectories in a faster way.