Xiao Zhi Gao

Artificial immune optimization methods and applications - a survey

Inspired by natural immune systems, artificial immune systems (AIS) are an emerging kind of computational intelligence paradigm. During the past decade, the AIS have gained great research interest in wide engineering fields. Artificial immune optimization (AIO) methods are an important partner of the AIS. They have been successfully applied to deal with numerous challenging optimization problems with superior performance over classical optimization techniques. This paper gives a concise survey on the recent progresses of the theory as well as applications of the AIO schemes, in which some representative approaches are briefly introduced and discussed.

Soft computing methods in motor fault diagnosis

Abstract During the last decade, soft computing (computational intelligence) has attracted great interest from different areas of research. In this paper, we give an overview on the recent developments in the emerging field of soft computing-based electric motor fault diagnosis. Several typical fault diagnosis schemes using neural networks, fuzzy logic, neural-fuzzy, and genetic algorithms, with descriptive diagrams as well as simplified algorithms are presented. Their advantages and disadvantages are compared and discussed. We conclude that soft computing methods have great potential in dealing with difficult fault detection and diagnosis problems.

Stability analysis of the simplest Takagi-Sugeno fuzzy control system using circle criterion

In this paper, we present two sufficient conditions in the frequency domain for the global stability analysis of the simplest Takagi-Sugeno (T-S) fuzzy control system, based on the circle criterion and its graphical interpretation. These conditions are significant in graphically designing the simplest T-S fuzzy controller in the frequency domain. Four numerical examples are provided to demonstrate the efficiency of our two frequency domain-based conditions. Furthermore, this T-S fuzzy controller and the linear proportional controllers are also compared. It is concluded that the simplest T-S fuzzy controller can outperform the linear proportional controllers in a noisy environment with external disturbances.

Power prediction in mobile communication systems using an optimal neural-network structure

Presents a novel neural-network-based predictor for received power level prediction in direct sequence code division multiple access (DS/CDMA) systems. The predictor consists of an adaptive linear element (Adaline) followed by a multilayer perceptron (MLP). An important but difficult problem in designing such a cascade predictor is to determine the complexity of the networks. We solve this problem by using the predictive minimum description length (PMDL) principle to select the optimal numbers of input and hidden nodes. This approach results in a predictor with both good noise attenuation and excellent generalization capability. The optimized neural networks are used for predictive filtering of very noisy Rayleigh fading signals with 1.8 GHz carrier frequency. Our results show that the optimal neural predictor can provide smoothed in-phase and quadrature signals with signal-to-noise ratio (SNR) gains of about 12 and 7 dB at the urban mobile speeds of 5 and 50 km/h, respectively. The corresponding power signal SNR gains are about 11 and 5 dB. Therefore, the neural predictor is well suitable for power control applications where ldquodelaylessrdquo noise attenuation and efficient reduction of fast fading are required.

A new metaheuristic optimisation algorithm motivated by elephant herding behaviour

In this paper, a new swarm-based metaheuristic algorithm, called elephant herding optimisation EHO, is proposed for solving global optimisation tasks, which is inspired by the herding behaviour of the elephant groups. In nature, the elephants belonging to different clans live together under the leadership of a matriarch, and the male elephants will leave their family group when growing up. These two behaviours can be modelled into two following operators: clan updating operator and separating operator. In EHO, the elephants are updated using its current position and matriarch through clan updating operator, and the separating operator is then implemented. Moreover, EHO has been benchmarked by 20 standard benchmarks, and two engineering cases in comparison with BBO, DE and GA. The results clearly establish the supremacy of EHO in finding the better function values on most test problems than those three algorithms. The code can be found in the website: http://www.mathworks.com/matlabcentral/fileexchange/53486.

Improved bat algorithm with optimal forage strategy and random disturbance strategy

Bat algorithm is a novel bio-inspired stochastic optimisation algorithm. However, due to the limited exploration and exploitation capabilities, the performance is not well when dealing with some multi-modal numerical problems. In this paper, optimal forage strategy is designed to guide the search direction for each bat and a random disturbance strategy is also employed to extend the global search pattern. To test the performance, CEC2013 benchmark test suit and four other evolutionary algorithms are employed to compare, simulation results show our modification is effective.

An ANFIS approach for evaluation of team-level service climate in GSD projects using Taguchi-genetic learning algorithm

ANFIS architecture for a multi-inputs and single output Sugeno model with fuzzy n rules. The GSD team-level service climate is one of the key determinants to achieve the outcome of global software development (GSD) projects.To evaluate the GSD team-level service climate and GSD project outcome relationship based on Adaptive Neuro-Fuzzy Inference System (ANFIS) with the Hybrid Taguchi-Genetic Learning Algorithm (HTGLA).The applicability and capability of HTGLA-based ANFIS approach is investigated through the real data sets obtained from Indian software industries. The GSD team-level service climate is one of the key determinants to achieve the outcome of global software development (GSD) projects from the software service outsourcing perspective. The main aim of this study is to evaluate the GSD team-level service climate and GSD project outcome relationship based on adaptive neuro-fuzzy inference system (ANFIS) with the genetic learning algorithm. For measuring the team-level service climate, the Hybrid Taguchi-Genetic Learning Algorithm (HTGLA) is adopted in the ANFIS, which is more appropriate to determine the optimal premise and consequent constructs by reducing the root-mean-square-error (RMSE) of service climate criteria. For measuring the GSD team-level service climate, synthesizing the literature reviews and consistent with the earlier studies on IT service climate which is classified into three main criterion: managerial practices (deliver quality of service), global service climate (measure overall perceptions), service leadership (goal setting, work planning, and coordination) which comprises 25 GSD team-level service climate attributes. The experimental results show that the optimal prediction error is obtained by the HTGLA-based ANFIS approach is 3.26%, which outperforms the earlier result that is the optimal prediction errors 4.41% and 5.75% determined, respectively, by ANFIS and statistical methods.

A new bio-inspired optimisation algorithm: Bird Swarm Algorithm

A new bio-inspired algorithm, namely Bird Swarm Algorithm (BSA), is proposed for solving optimisation applications. BSA is based on the swarm intelligence extracted from the social behaviours and social interactions in bird swarms. Birds mainly have three kinds of behaviours: foraging behaviour, vigilance behaviour and flight behaviour. Birds may forage for food and escape from the predators by the social interactions to obtain a high chance of survival. By modelling these social behaviours, social interactions and the related swarm intelligence, four search strategies associated with five simplified rules are formulated in BSA. Simulations and comparisons based on eighteen benchmark problems demonstrate the effectiveness, superiority and stability of BSA. Some proposals for future research about BSA are also discussed.

Fusion of clonal selection algorithm and harmony search method in optimisation of fuzzy classification systems

This paper presents a hybrid optimisation method based on the fusion of the clonal selection algorithm (CSA) and harmony search (HS) technique. The CSA is employed to improve the harmony memory members in the HS method. The hybrid optimisation algorithm is further used to optimise Sugeno fuzzy classification systems for the Fisher Iris data and wine data classification. Computer simulations results demonstrate the remarkable effectiveness of our new approach.

A Hybrid Particle Swarm Optimization Method

This paper proposes a hybrid particle swarm optimization (PSO) method, which is based on the fusion of the PSO, clonal selection algorithm (CSA), and mind evolutionary computation (MEC). The clone function borrowed from the CSA and MEC-characterized similartaxis and dissimilation operations are embedded in the original PSO. Simulations of nonlinear function optimization are made to compare this hybrid PSO with the regular PSO. It has been demonstrated that our hybrid algorithm can achieve a better convergence performance, and provide diverse solutions to multi-model optimization problems.