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Article
Publication date: 19 June 2019

Wensheng Xiao, Qi Liu, Linchuan Zhang, Kang Li and Lei Wu

Bat algorithm (BA) is a global optimization method, but has a worse performance on engineering optimization problems. The purpose of this study is to propose a novel…

Abstract

Purpose

Bat algorithm (BA) is a global optimization method, but has a worse performance on engineering optimization problems. The purpose of this study is to propose a novel chaotic bat algorithm based on catfish effect (CE-CBA), which can effectively deal with optimization problems in real-world applications.

Design/methodology/approach

Incorporating chaos strategy and catfish effect, the proposed algorithm can not only enhance the ability for local search but also improve the ability to escape from local optima traps. On the one hand, the performance of CE-CBA has been evaluated by a set of numerical experiment based on classical benchmark functions. On the other hand, five benchmark engineering design problems have been also used to test CE-CBA.

Findings

The statistical results of the numerical experiment show the significant improvement of CE-CBA compared with the standard algorithms and improved bat algorithms. Moreover, the feasibility and effectiveness of CE-CBA in solving engineering optimization problems are demonstrated.

Originality/value

This paper proposed a novel BA with two improvement strategies including chaos strategy and catfish effect for the first time. Meanwhile, the proposed algorithm can be used to solve many real-world engineering optimization problems with several decision variables and constraints.

Details

Engineering Computations, vol. 36 no. 5
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 16 March 2021

Aws Abdulsalam Najm, Ibraheem Kasim Ibraheem, Amjad J. Humaidi and Ahmad Taher Azar

The hybrid control system of the nonlinear PID (NLPID) controller and improved active disturbance rejection control (IADRC) are proposed for stabilization purposes for a…

Abstract

Purpose

The hybrid control system of the nonlinear PID (NLPID) controller and improved active disturbance rejection control (IADRC) are proposed for stabilization purposes for a 6-degree freedom (DoF) quadrotor system with the existence of exogenous disturbances and system uncertainties.

Design/methodology/approach

IADRC units are designed for the altitude and attitude systems, while NLPID controllers are designed for the xy position system on the quadrotor nonlinear model. The proposed controlling scheme is implemented using MATLAB/Simulink environment and is compared with the traditional PID controller and NLPID controller.

Findings

Different tests have been done, such as step reference tracking, hovering mode, trajectory tracking, exogenous disturbances and system uncertainties. The simulation results showed the demonstrated performance and stability gained by using the proposed scheme as compared with the other two controllers, even when the system was exposed to different disturbances and uncertainties.

Originality/value

The study proposes an NLPID-IADRC scheme to stabilize the motion of the quadrotor system while tracking a specified trajectory in the presence of exogenous disturbances and parameter uncertainties. The proposed multi-objective Output Performance Index (OPI) was used to obtain the optimum integrated time of the absolute error for each subsystem, UAV quadrotor system energy consumption and for minimizing the chattering phenomenon by adding the integrated time absolute of the control signals.

Details

International Journal of Intelligent Unmanned Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2049-6427

Keywords

Article
Publication date: 6 February 2020

Sajad Ahmad Rather and P. Shanthi Bala

The purpose of this paper is to investigate the performance of chaotic gravitational search algorithm (CGSA) in solving mechanical engineering design frameworks including…

Abstract

Purpose

The purpose of this paper is to investigate the performance of chaotic gravitational search algorithm (CGSA) in solving mechanical engineering design frameworks including welded beam design (WBD), compression spring design (CSD) and pressure vessel design (PVD).

Design/methodology/approach

In this study, ten chaotic maps were combined with gravitational constant to increase the exploitation power of gravitational search algorithm (GSA). Also, CGSA has been used for maintaining the adaptive capability of gravitational constant. Furthermore, chaotic maps were used for overcoming premature convergence and stagnation in local minima problems of standard GSA.

Findings

The chaotic maps have shown efficient performance for WBD and PVD problems. Further, they have depicted competitive results for CSD framework. Moreover, the experimental results indicate that CGSA shows efficient performance in terms of convergence speed, cost function minimization, design variable optimization and successful constraint handling as compared to other participating algorithms.

Research limitations/implications

The use of chaotic maps in standard GSA is a new beginning for research in GSA particularly convergence and time complexity analysis. Moreover, CGSA can be used for solving the infinite impulsive response (IIR) parameter tuning and economic load dispatch problems in electrical sciences.

Originality/value

The hybridization of chaotic maps and evolutionary algorithms for solving practical engineering problems is an emerging topic in metaheuristics. In the literature, it can be seen that researchers have used some chaotic maps such as a logistic map, Gauss map and a sinusoidal map more rigorously than other maps. However, this work uses ten different chaotic maps for engineering design optimization. In addition, non-parametric statistical test, namely, Wilcoxon rank-sum test, was carried out at 5% significance level to statistically validate the simulation results. Besides, 11 state-of-the-art metaheuristic algorithms were used for comparative analysis of the experimental results to further raise the authenticity of the experimental setup.

Article
Publication date: 25 November 2013

Yanming Fan and Ming Li

The purpose of this paper is to present weighted Euclidean distance for measuring whether the fitting of projective transformation matrix is more reliable in feature-based…

Abstract

Purpose

The purpose of this paper is to present weighted Euclidean distance for measuring whether the fitting of projective transformation matrix is more reliable in feature-based image stitching.

Design/methodology/approach

The hybrid model of weighted Euclidean distance criterion and intelligent chaotic genetic algorithm (CGA) is established to achieve a more accurate matrix in image stitching. Feature-based image stitching is used in this paper for it can handle non-affine situations. Scale invariant feature transform is applied to extract the key points, and the false points are excluded using random sampling consistency (RANSAC) algorithm.

Findings

This work improved GA by combination with chaos's ergodicity, so that it can be applied to search a better solution on the basis of the matrix solved by Levenberg-Marquardt. The addition of an external loop in RANSAC can help obtain more accurate matrix with large probability. Series of experimental results are presented to demonstrate the feasibility and effectiveness of the proposed approaches.

Practical implications

The modified feature-based method proposed in this paper can be easily applied to practice and can obtain a better image stitching performance with a good robustness.

Originality/value

A hybrid model of weighted Euclidean distance criterion and CGA is proposed for optimization of projective transformation matrix in image stitching. The authors introduce chaos theory into GA to modify its search strategy.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 6 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 6 March 2017

Soyinka Olukunle Kolawole and Duan Haibin

Keeping satellite position within close tolerances is key for the utilization of satellite formations for space missions. The presence of perturbation forces makes control…

Abstract

Purpose

Keeping satellite position within close tolerances is key for the utilization of satellite formations for space missions. The presence of perturbation forces makes control inevitable if such mission objective is to be realised. Various approaches have been used to obtain feedback controller parameters for satellites in a formation; this paper aims to approach the problem of estimating the optimal feedback parameter for a leader–follower pair of satellites in a small eccentric orbit using nature-based search algorithms.

Design/methodology/approach

The chaotic artificial bee colony algorithm is a variant of the basic artificial bee colony algorithm. The algorithm mimics the behaviour of bees in their search for food sources. This paper uses the algorithm in optimizing feedback controller parameters for a satellite formation control problem. The problem is formulated to optimize the controller parameters while minimizing a fuel- and state-dependent cost function. The dynamical model of the satellite is based on Gauss variational equations with J2 perturbation. Detailed implementation of the procedure is provided, and experimental results of using the algorithm are also presented to show feasibility of the method.

Findings

The experimental results indicate the feasibility of this approach, clearly showing the effective control of the transients that arise because of J2 perturbation.

Originality/value

This paper applied a swarm intelligence approach to the problem of estimating optimal feedback control parameter for a pair of satellites in a formation.

Details

Aircraft Engineering and Aerospace Technology, vol. 89 no. 2
Type: Research Article
ISSN: 1748-8842

Keywords

Article
Publication date: 8 June 2015

Ahmad Mozaffari, Nasser L. Azad and Alireza Fathi

The purpose of this paper is to examine the structural and computational potentials of a powerful class of neural networks (NNs), called multiple-valued logic neural…

Abstract

Purpose

The purpose of this paper is to examine the structural and computational potentials of a powerful class of neural networks (NNs), called multiple-valued logic neural networks (MVLNN), for predicting the behavior of phenomenological systems with highly nonlinear dynamics. MVLNNs are constructed based on the integration of a number of neurons working based on the principle of multiple-valued logics. MVLNNs possess some particular features, namely complex-valued weights, input, and outputs coded by kth roots of unity, and a continuous activation as a mean for transferring numbers from complex spaces to trigonometric spaces, which distinguish them from most of the existing NNs.

Design/methodology/approach

The presented study can be categorized into three sections. At the first part, the authors attempt at providing the mathematical formulations required for the implementation of ARX-based MVLNN (AMVLNN). In this context, it is indicated that how the concept of ARX can be used to revise the structure of MVLNN for online applications. Besides, the stepwise formulation for the simulation of Chua’s oscillatory map and multiple-valued logic-based BP are given. Through an analysis, some interesting characteristics of the Chua’s map, including a number of possible attractors of the state and sequences generated as a function of time, are given.

Findings

Based on a throughout simulation as well as a comprehensive numerical comparative study, some important features of AMVLNN are demonstrated. The simulation results indicate that AMVLNN can be employed as a tool for the online identification of highly nonlinear dynamic systems. Furthermore, the results show the compatibility of the Chua’s oscillatory system with BP for an effective tuning of the synaptic weights. The results also unveil the potentials of AMVLNN as a fast, robust, and efficient control-oriented model at the heart of NMPC control schemes.

Originality/value

This study presents two innovative propositions. First, the structure of MVLNN is modified based on the concept of ARX system identification programming to suit the base structure for coping with chaotic and highly nonlinear systems. Second, the authors share the findings about the learning characteristics of MVLNNs. Through an exhaustive comparative study and considering different rival methodologies, a novel and efficient double-stage learning strategy is proposed which remarkably improves the performance of MVLNNs. Finally, the authors describe the outline of a novel formulation which prepares the proposed AMVLNN for applications in NMPC controllers for dynamic systems.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 8 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 9 November 2012

Soheil Ganjefar and Mojtaba Alizadeh

The power system is complex multi‐component dynamic system with many operational levels made up of a wide range of energy sources with many interaction points. Low…

Abstract

Purpose

The power system is complex multi‐component dynamic system with many operational levels made up of a wide range of energy sources with many interaction points. Low frequency oscillations are observed when large power systems are interconnected by relatively weak tie lines. These oscillations may sustain and grow to cause system separation if no adequate damping is available. The present paper aims to propose an on‐line self‐learning PID (OLSL‐PID) controller in order to damp the low frequency power system oscillations in a single‐machine system.

Design/methodology/approach

The proposed OLSL‐PID is used as a controller in order to damp the low frequency power system oscillations. It has a local nature because of its powerful adaption process based on back‐propagation (BP) algorithm that is implemented through an adaptive self‐recurrent wavelet neural network identifier (ASRWNNI). In fact PID controller parameters are updated in on‐line mode, using BP algorithm based on the information provided by the ASRWNNI which is a powerful fast‐acting identifier because of its local nature, self‐recurrent structure and stable training algorithm with ALRs based on discrete lyapunov stability theorem.

Findings

The proposed control scheme is applied to a single machine infinite bus power system under different operating conditions and disturbances. The nonlinear time‐domain simulation results are promising and show the effectiveness and robustness of the proposed controller and also reveal that: because of the high adaptability, the local behavior and high flexibility of the OLSL‐PID controller, it can be damp the low frequency oscillations in the best possible manner and significantly improves the stability performance of the system.

Originality/value

The proposed controller adaption process is done in each sampling period using a powerful adaption law based on BP algorithm. Also during the process the system sensitivity is provided by a powerful fast‐acting identifier. As an alternative to multi‐layer perceptron neural network, self‐recurrent wavelet neural networks (SRWNNs) which combine the properties such as attractor dynamics of recurrent neural network and the fast convergence of the wavelet neural network were proposed to identify synchronous generator. Also to help the OLSL‐PID stability first, PID parameters tuning problem under a wide range of operating conditions is converted to an optimization problem which solved by a chaotic optimization algorithm (COA), and afterwards PID controller is hooked up in the system and on‐line tuning is done in each sampling period.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 31 no. 6
Type: Research Article
ISSN: 0332-1649

Keywords

Article
Publication date: 20 April 2022

Binghai Zhou, Qi Yi, Xiujuan Li and Yutong Zhu

This paper aims to investigate a multi-objective electric vehicle’s (EV’s) synergetic scheduling problem in the automotive industry, where a synergetic delivery mechanism…

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Abstract

Purpose

This paper aims to investigate a multi-objective electric vehicle’s (EV’s) synergetic scheduling problem in the automotive industry, where a synergetic delivery mechanism to coordinate multiple EVs is proposed to fulfill part feeding tasks.

Design/methodology/approach

A chaotic reference-guided multi-objective evolutionary algorithm based on self-adaptive local search (CRMSL) is constructed to deal with the problem. The proposed CRMSL benefits from the combination of reference vectors guided evolutionary algorithm (RVEA) and chaotic search. A novel directional rank sorting procedure and a self-adaptive energy-efficient local search strategy are then incorporated into the framework of the CRMSL to obtain satisfactory computational performance.

Findings

The involvement of the chaotic search and self-adaptive energy-efficient local search strategy contributes to obtaining a stronger global and local search capability. The computational results demonstrate that the CRMSL achieves better performance than the other two well-known benchmark algorithms in terms of four performance metrics, which is inspiring for future researches on energy-efficient co-scheduling topics in manufacturing industries.

Originality/value

This research fully considers the cooperation and coordination of handling devices to reduce energy consumption, and an improved multi-objective evolutionary algorithm is creatively applied to solve the proposed engineering problem.

Details

Engineering Computations, vol. 39 no. 7
Type: Research Article
ISSN: 0264-4401

Keywords

Article
Publication date: 13 September 2021

Manik Chandra and Rajdeep Niyogi

This paper aims to solve the web service selection problem using an efficient meta-heuristic algorithm. The problem of selecting a set of web services from a large-scale…

Abstract

Purpose

This paper aims to solve the web service selection problem using an efficient meta-heuristic algorithm. The problem of selecting a set of web services from a large-scale service environment (web service repository) while maintaining Quality-of-Service (QoS), is referred to as web service selection (WSS). With the explosive growth of internet services, managing and selecting the proper services (or say web service) has become a pertinent research issue.

Design/methodology/approach

In this paper, to address WSS problem, the authors propose a new modified fruit fly optimization approach, called orthogonal array-based learning in fruit fly optimizer (OL-FOA). In OL-FOA, they adopt a chaotic map to initialize the population; they add the adaptive DE/best/2mutation operator to improve the exploration capability of the fruit fly approach; and finally, to improve the efficiency of the search process (by reducing the search space), the authors use the orthogonal learning mechanism.

Findings

To test the efficiency of the proposed approach, a test suite of 2500 web services is chosen from the public repository. To establish the competitiveness of the proposed approach, it compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC). The empirical results show that the proposed approach outperforms its counterparts in terms of response time, latency, availability and reliability.

Originality/value

In this paper, the authors have developed a population-based novel approach (OL-FOA) for the QoS aware web services selection (WSS). To justify the results, the authors compared against four other meta-heuristic approaches (including classical as well as state-of-the-art), namely, fruit fly optimization (FOA), differential evolution (DE), modified artificial bee colony algorithm (mABC) and global-best ABC (GABC) over the four QoS parameter response time, latency, availability and reliability. The authors found that the approach outperforms overall competitive approaches. To satisfy all objective simultaneously, the authors would like to extend this approach in the frame of multi-objective WSS optimization problem. Further, this is declared that this paper is not submitted to any other journal or under review.

Details

International Journal of Pervasive Computing and Communications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 24 June 2013

Gai-Ge Wang, Amir Hossein Gandomi and Amir Hossein Alavi

To improve the performance of the krill herd (KH) algorithm, in this paper, a series of chaotic particle-swarm krill herd (CPKH) algorithms are proposed for solving…

Abstract

Purpose

To improve the performance of the krill herd (KH) algorithm, in this paper, a series of chaotic particle-swarm krill herd (CPKH) algorithms are proposed for solving optimization tasks within limited time requirements. The paper aims to discuss these issues.

Design/methodology/approach

In CPKH, chaos sequence is introduced into the KH algorithm so as to further enhance its global search ability.

Findings

This new method can accelerate the global convergence speed while preserving the strong robustness of the basic KH.

Originality/value

Here, 32 different benchmarks and a gear train design problem are applied to tune the three main movements of the krill in CPKH method. It has been demonstrated that, in most cases, CPKH with an appropriate chaotic map performs superiorly to, or at least highly competitively with, the standard KH and other population-based optimization methods.

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