Adaptive Clonal Selection Algorithm Research Articles

Hierarchical control strategies for multi-mode steering system of emergency rescue vehicle

It is difficult for the traditional emergency rescue vehicles (ERVs) to pass through narrow and complicated areas quickly, which leads to the ERVs missing the best time to rescue disasters seriously. How to improve the trafficability and mobility of vehicles effectively in narrow and complex areas has become an urgent technical demand for the ERVs. In this context, this paper proposes a multi-mode steering system to realize four steering modes of the ERVs. A fire rescue prototype vehicle is developed, and its multi-mode steering system is designed in detail. The hierarchical control strategies of the multi-mode steering system consist of the upper controller and the lower controller. The upper controller is responsible for assigning the steering angles of four wheels of the ERVs based on the Ackerman principle. The lower controller is used to control the ERVs to turn four wheels independently based on the electro-hydraulic fractional order PID (FOPID) control strategy, and then the parameters of the FOPID controller are optimized by the adaptive clonal selection algorithm. The multi-mode steering system proposed was tested on the homemade fire rescue prototype vehicle. The numerical simulations and experiments were conducted to verify the effectiveness of the proposed multi-mode steering system.

An adaptive clonal selection algorithm with multiple differential evolution strategies

Clonal selection algorithms have provided significant insights into numerical optimization problems. However, most mutation operators in conventional clonal selection algorithms have semi-blindness and lack an effective guidance mechanism, which has thus become one of the important factors restricting the performance of algorithms. To address these problems, this study develops an improved clonal selection algorithm called an adaptive clonal selection algorithm with multiple differential evolution strategies (ADECSA) with three features: (1) an adaptive mutation strategy pool based on its historical records of success is introduced to guide the immune response process effectively; (2) an adaptive population resizing method is adopted to speed up convergence; and (3) a premature convergence detection method and a stagnation detection method are proposed to alleviate premature convergence and stagnation problems in the evolution by enhancing the diversity of the population. Experimental results on a wide variety of benchmark functions demonstrate that our proposed method achieves better performance than both state-of-the-art clonal selection algorithms and differential evolution algorithms. Especially in the comparisons with other clonal selection algorithms, our proposed method outperforms at least 23 out of 30 benchmark functions from the CEC2014 test suite.

Sliding Mode Switch Control of Adjustable Hydro-Pneumatic Suspension based on Parallel Adaptive Clonal Selection Algorithm

The hydro-pneumatic suspension, as a widely used suspension for heavy vehicles, has been taken seriously by researchers for a long time because it is crucial in terms of handling stability, riding comfort, and driving safety of these vehicles. Most previous studies only discussed the control of ride comfort or vehicle handling stability of the suspension system separately. This article proposes a dynamic switch control strategy which can switch between ride comfort and handling stability controllers under different road surfaces and driving conditions. The load transfer ratio (LTR) is selected as the switch performance index, and it is calculated through a six-degrees-of-freedom (6-DOF) model. The ride comfort and handling stability controller of the hydro-pneumatic suspension are designed based on the sliding mode control theory. The objective functions of parameters optimization of the sliding mode controller (SMC) are obtained by means of analytic hierarchy process (AHP), and then the controller’s parameters are optimized by the parallel adaptive clonal selection algorithm (PACSA). The simulation results based on MATLAB/Simulink show that: (1) the PACSA performs better than a genetic algorithm in terms of the parameters optimization of the SMC; (2) the proposed switch control strategy can simultaneously improve the ride comfort and handling stability under several typical steering maneuvers and various road profiles compared with the conventional SMC-controlled suspension.

Constrained Transmit Beampattern Design Using a Correlated LFM-PC Waveform Set in MIMO Radar.

This paper considers the design of a desired transmit beampattern under the good ambiguity function constraint using a correlated linear frequency modulation-phase coded (LFM-PC) waveform set in multiple-input-multiple-output (MIMO) radar. Different from most existing beampattern design approaches, we propose using the LFM-PC waveform set to conquer the challenging problem of synthesizing waveforms with constant-envelope and easy-generation properties, and, meanwhile, solve the hard constraint of a good ambiguity behaviour. First, the ambiguity function of the LFM-PC waveform set is derived, and the superiority of LFM-PC waveforms over LFM and PC waveforms is verified. The temporal and spatial characteristic analysis of the LFM-PC waveform set demonstrates that both the transmit beampattern and sidelobe level are mainly affected by the frequency intervals, bandwidths, and phase-coded sequences of the LFM-PC waveform set. Finally, the constrained beampattern design problem is formulated by optimizing these parameters for desired beampatterns and low sidelobes at different doppler frequencies, which is a bi-objective optimization problem. To solve this, we propose a joint optimization strategy followed by a mandatory optimization, where the sequence quadratic programming (SQP) algorithm and adaptive clonal selection (ACS) algorithm are exploited iteratively. The simulation results demonstrate the efficiency of our proposed method.

Design and analysis of frequency division phase-coded waveforms for MIMO over-the-horizon radars

This paper addresses the optimization problem of frequency division phase-coded (FDPC) waveforms for improving the detection performance in multiple-input multiple-output over-the-horizon radars (MIMO-OTHR). A sparse frequency division complementary chaotic modulation (SFDCCM) waveform is proposed and analyzed. First, the basic structure of the FDPC waveform is presented, and its MIMO ambiguity function is derived to provide guidance on further waveform optimization. It turns out that the modulation codes and carrier frequencies are two key factors for good waveform performance. Then, the chaotic sequences are introduced due to their unique advantages, and an adaptive clonal selection algorithm is utilized to obtain a pair of complementary chaotic sequences to suppress the range sidelobes. The optimized complementary pair is modulated on different carrier frequencies. As the available spectrum of the MIMO-OTHR is discontinuously distributed, the carrier frequencies are generally sparse and should be properly selected according to the real-time frequency spectrum monitoring (RTFSM) system. Finally, the SFDCCM is proposed based on the FDPC structure. The pulse compression results of the SFDCCM are compared with typical waveforms. Four cases of carrier frequency selection in the SFDCCM and the corresponding doppler tolerance are analyzed. Simulation results validate the superiority of the proposed waveform in this paper.

Chaotic Phase-Coded Waveforms With Space-Time Complementary Coding for MIMO Radar Applications

A framework for designing orthogonal chaotic phase-coded waveforms with space-time complementary coding is proposed for multiple-input multiple-output (MIMO) radar applications. The phase-coded waveform set to be transmitted is generated with an arbitrary family size and an arbitrary code length by using chaotic sequences. Due to the properties of chaos, this chaotic waveform set has many advantages in performance, such as anti-interference and low probability of intercept. However, it cannot be directly exploited due to the high range sidelobes, mutual interferences, and Doppler intolerance. In order to widely implement it in practice, we optimize the chaotic phase-coded waveform set from two aspects. First, the autocorrelation property of the waveform is improved by transmitting complementary chaotic phase-coded waveforms, and an adaptive clonal selection algorithm is utilized to optimize a pair of complementary chaotic phase-coded pulses. Second, the crosscorrelation among different waveforms is eliminated by implementing space-time coding into the complementary pulses. Moreover, to enhance the detection ability for moving targets in MIMO radars, a method of weighting different pulses by a null space vector is utilized at the receiver to compensate the interpulse Doppler phase shift and accumulate different pulses coherently. Simulation results demonstrate the efficiency of our proposed method.

Multiperiod Hierarchical Location Problem of Transit Hub in Urban Agglomeration Area

With the rapid urbanization in developing countries, urban agglomeration area (UAA) forms. Also, transportation demand in UAA grows rapidly and presents hierarchical feature. Therefore, it is imperative to develop models for transit hubs to guide the development of UAA and better meet the time-varying and hierarchical transportation demand. In this paper, the multiperiod hierarchical location problem of transit hub in urban agglomeration area (THUAA) is studied. A hierarchical service network of THUAA with a multiflow, nested, and noncoherent structure is described. Then a multiperiod hierarchical mathematical programming model is proposed, aiming at minimizing the total demand weighted travel time. Moreover, an improved adaptive clonal selection algorithm is presented to solve the model. Both the model and algorithm are verified by the application to a real-life problem of Beijing-Tianjin-Hebei Region in China. The results of different scenarios in the case show that urban population migration has a great impact on the THUAA location scheme. Sustained and appropriate urban population migration helps to reduce travel time for urban residents.

Application of adaptive clonal selection algorithm to solve multi-objective optimal power flow with wind energy conversion systems

Application of adaptive clonal selection algorithm to solve multi-objective optimal power flow with wind energy conversion systems

Application of adaptive clonal selection algorithm to solve multi-objective optimal power flow with wind energy conversion systems

This paper presents the application of adaptive clonal selection algorithm to solve single and multi-objective optimal power flow (OPF) problems with the incorporation of wind energy conversion systems. As the wind power is intermittent in nature it requires an appropriate tool for OPF problem. Minimisation of generation cost of thermal as well as wind units, transmission loss and voltage stability index are considered as three conflicting objectives for optimisation. A fast elitist non-dominated sorting and crowding distance techniques have been used to find and manage the Pareto optimal front. Further, a fuzzy-based mechanism has been applied to select a best compromise solution from the Pareto set. The proposed method has been tested on standard IEEE 30-bus test system having three conventional and three wind power generators. The simulation results are compared with three other standard algorithms such as non-dominated sorting genetic algorithm-II, multi-objective particle swarm optimisation and multi-objective differential evolution.

Fractional-order control of active suspension actuator based on parallel adaptive clonal selection algorithm

In current study, a new approach for vehicle active suspension actuator control is reported. For this approach, a nonlinear model for an electro-hydraulic servo actuator and quarter suspension system is established. An Oustaloup recursive approximation (ORA) Fractionalorder proportional–integral–derivative (FOPID) controller is designed to control the displacement of the actuator, and controller parameters are optimized based on error integral criteria using the Parallel adaptive clonal selection algorithm (PACSA). The research indicates that PACSA can tune parameters of the FOPID controller accurately and efficiently, and the tuned FOPID controller performs better in terms of response speed and tracking accuracy than a regular Proportional–integral–derivative (PID) controller. The performance of the active suspension system that uses the FOPID actuator is obviously superior to that of the active suspension system that uses a PID actuator and a passive suspension system.

Artificial immune system based on adaptive clonal selection for feature selection and parameters optimisation of support vector machines

ABSTRACTArtificial immune system (AIS) algorithm based on clonal selection method can be defined as a soft computing method inspired by theoretical immune system in order to solve science and engineering problems. Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting in the SVM training procedure along with the feature selection significantly impacts on the classification accuracy rate. In this study, AIS based on Adaptive Clonal Selection (AISACS) algorithm has been used to optimise the SVM parameters and feature subset selection without degrading the SVM classification accuracy. Several public datasets of University of California Irvine machine learning (UCI) repository are employed to calculate the classification accuracy rate in order to evaluate the AISACS approach then it was compared with grid search algorithm and Genetic Algorithm (GA) approach. The experimental results show that the feature reduction rate and running time of the AISACS approach are better than the GA approach.

Multi-objective adaptive clonal selection algorithm for solving optimal power flow problem with load uncertainty

This paper presents a multi-objective adaptive clonal selection algorithm MOACSA for solving optimal power flow OPF problem with load uncertainty. The multi-objective OPF MOOPF problem is generally formulated with minimisation of several objectives by satisfying various constraints. A fast elitist non-dominated sorting and the crowded distance concept have been used to find and manage the Pareto optimal front. Finally, a fuzzy-based mechanism is used for selecting the best compromise solution. The proposed MOACSA method is tested on IEEE 30-bus test system with minimisation of fuel cost, loss and L-index as objectives. Simulation studies are carried out with normal load operation and load uncertainty conditions for MOOPF. The MOACSA method results are compared with non-dominated sorting genetic algorithm NSGA-II, multi-objective particle swarm optimisation MOPSO and multi-objective differential evolution MODE methods without load uncertainty.

Multi-objective adaptive clonal selection algorithm for solving optimal power flow considering multi-type FACTS devices and load uncertainty

This paper presents a solution to multi-objective optimal power flow (MOOPF) problem using an adaptive clonal selection algorithm (ACSA) to minimise generation cost, transmission loss and voltage stability index (L-index) in the presence of multi-type FACTS devices in power systems. The proposed approach utilizes clonal selection principle and evolutionary concept which performs cloning of antibodies followed by hyper maturation. In this algorithm, a non-dominated sorting and crowding distance have been used to find and manage Pareto optimal front. Various voltage source converter (VSC) based multi-type FACTS devices such as UPFC, IPFC and GUPFC are considered and incorporated as power injection models in multi-objective optimisation problem formulation. The proposed multi-objective adaptive clonal selection algorithm (MOACSA) has been tested on standard IEEE 30-bus test system with FACTS devices. The results obtained from the proposed MOACSA approach are compared with implementation of standard algorithms namely NSGA-II, MOPSO and MODE.

Experimental Verification of Parameter Identification Method based on Symbolic Time Series Analysis and Adaptive Immune Clonal Selection Algorithm

The parameter identification method based on symbolic time series analysis (STSA) and adaptive immune clonal selection algorithm (AICSA) was experimentally verified using a 5-story experimental model structure. In the experimental verification, both single and multiple damage scenarios were studied. A 5-story structure was initially healthy with all original columns intact. The single-damage case, the double-damage or the triple-damage case was simulated by replacing the columns of one, two or three different floors, respectively. The experimental results have shown that the parameter identification method based on STSA and AICSA can successfully identify structure parameters only utilizing measured acceleration information for various damage scenarios under different excitation conditions. The proposed approach was shown promising for application of SHM on buildings.

Robust optimization model and algorithm for railway freight center location problem in uncertain environment.

Railway freight center location problem is an important issue in railway freight transport programming. This paper focuses on the railway freight center location problem in uncertain environment. Seeing that the expected value model ignores the negative influence of disadvantageous scenarios, a robust optimization model was proposed. The robust optimization model takes expected cost and deviation value of the scenarios as the objective. A cloud adaptive clonal selection algorithm (C-ACSA) was presented. It combines adaptive clonal selection algorithm with Cloud Model which can improve the convergence rate. Design of the code and progress of the algorithm were proposed. Result of the example demonstrates the model and algorithm are effective. Compared with the expected value cases, the amount of disadvantageous scenarios in robust model reduces from 163 to 21, which prove the result of robust model is more reliable.

Synthetic Optimization Model and Algorithm for Railway Freight Center Station Location and Wagon Flow Organization Problem

The railway freight center stations location and wagon flow organization in railway transport are interconnected, and each of them is complicated in a large-scale rail network. In this paper, a two-stage method is proposed to optimize railway freight center stations location and wagon flow organization together. The location model is present with the objective to minimize the operation cost and fixed construction cost. Then, the second model of wagon flow organization is proposed to decide the optimal train service between different freight center stations. The location of the stations is the output of the first model. A heuristic algorithm that combined tabu search (TS) with adaptive clonal selection algorithm (ACSA) is proposed to solve those two models. The numerical results show the proposed solution method is effective.

Multi-objective adaptive Clonal selection algorithm for solving environmental/economic dispatch and OPF problems with load uncertainty

This paper presents a new multi objective optimization approach based on Adaptive Clonal Selection Algorithm (ACSA) to solve complex Environmental/Economic Dispatch (EED) problem of thermal generators in power system. The proposed methodology also incorporates the power demand equality constraint and ensures various operating constraint limits while solving EED problem. In this algorithm an adaptive Clonal selection principle with non-dominated sorting technique and crowding distance has been used to find and manage Pareto-optimal set. Clonal selection principle is one of the models used to incorporate the behavior of the artificial immune system. The biological principles of clone generation, proliferation and maturation are mimicked and incorporated into this algorithm. To show the effectiveness of the proposed Multi Objective Adaptive Clonal Selection Algorithm (MOACSA) in solving EED problem two types of test systems have been considered with various objectives. These includes an IEEE 30-bus 6 unit test system and an 82-bus 10 unit Indian utility real life power system network for solving EED problem without and with load uncertainty. Simulation results are compared by implementation of three other standard algorithms such as Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO) and Multi-Objective Differential Evaluation (MODE) methods.

Poly‐phase codes optimisation for multi‐input–multi‐output radars

Multi-input–multi-output (MIMO) radar can fundamentally improve radar performance by using diversity technique. A group of specially designed signals, which usually are orthogonal, are required to be transmitted for MIMO radar obtaining excellent diversity performance. Although some well orthogonal codes have been provided, they are mostly Doppler sensitive, and their side-lobes level also need to be improved. In this study, the poly-phase codes model is presented and the optimisation problem is then analysed. An adaptive clonal selection algorithm is proposed to numerically optimise such poly-phase coded orthogonal signals. To obtain low level of aperiodic autocorrelation side lobe and cross correlation as well as good Doppler shift tolerance, sustainable Doppler shifts are introduced into the optimisation course. Numerical simulation results show the superior correlation and Doppler tolerance performances comparing with some well known codes.

Complementary‐based chaotic phase‐coded waveforms design for MIMO radar

This study deals with orthogonal waveform design for multi-input multi-output (MIMO) radar. Chaos is introduced to generate orthogonal phase-coded waveforms of arbitrary coded length for arbitrary number of transmitters. However, most of these chaos-based waveforms suffer high range sidelobe level and intrapulse Doppler intolerance for pulse compression. A complementary transmitting structure is proposed for the MIMO radar to supress these high range sidelobes, and an adaptive clonal selection algorithm is introduced to search for the optimal complementary codes of the transmitted chaotic phase-coded waveform. This algorithm proves that it is convergent in theory as well. A multiple hypotheses approach is introduced at the transmitters to estimate and then compensate for the intrapulse Doppler phase shift for MIMO radar pulse compression countering the intrapulse Doppler intolerance. Numerical simulations with four typical chaotic maps are carried out and compared with Deng's codes, which validate the proposed methods of this study.

MULTI-OBJECTIVE ADAPTIVE CLONAL SELECTION ALGORITHM FOR SOLVING OPF WITH WECS AND LOAD UNCERTAINTY

In this paper a solution to multi-objective optimal power flow (OPF) problem using an adaptive clonal selection algorithm (ACSA) with the incorporation of wind energy conversion system (WECS) is presented.