Immune Clonal Selection Algorithm Research Articles

Retraction Note: A study on car flow organization in the loading end of heavy haul railway based on immune clonal selection algorithm

Retraction Note: A study on car flow organization in the loading end of heavy haul railway based on immune clonal selection algorithm

Locating and sizing of charging station based on neighborhood mutation immune clonal selection algorithm

Locating and sizing of charging station based on neighborhood mutation immune clonal selection algorithm

Intelligent immune clonal optimization algorithm for pulmonary nodule classification.

Computer-aided diagnosis (CAD) of pulmonary nodules is an effective approach for early detection of lung cancers, and pulmonary nodule classification is one of the key issues in the CAD system. However, CAD has the problems of low accuracy and high false-positive rate (FPR) on pulmonary nodule classification. To solve these problems, a novel method using intelligent immune clonal selection and classification algorithm is proposed and developed in this work. First, according to the mechanism and characteristics of chaotic motion with a logistic mapping, the proposed method utilizes the characteristics of chaotic motion and selects the control factor of the optimal chaotic state, to generate an initial population with randomness and ergodicity. The singleness problem of the initial population of the immune algorithm was solved by the proposed method. Second, considering on the characteristics of Gaussian mutation operator (GMO) with a small scale, and Cauchy mutation operator (CMO) with a big scale, an intelligent mutation strategy is developed, and a novel control factor of the mutation is designed. Therefore, a Gauss-Cauchy hybrid mutation operator is designed. Ultimately, in this study, the intelligent immune clonal optimization algorithm is proposed and developed for pulmonary nodule classification. To verify its accuracy, the proposed method was used to analyze 90 CT scans with 652 nodules. The experimental results revealed that the proposed method had an accuracy of 97.87% and produced 1.52 false positives per scan (FPs/scan), indicating that the proposed method has high accuracy and low FPR.

A novel hybrid immune clonal selection algorithm for the constrained corridor allocation problem

Aiming at the lack of relevant research on relationship constraints between facilities in the corridor allocation problem (CAP). In this paper, fixed position constraints and ordering constraints are considered in CAP, and the logistics cost is minimized. Considering that the existing search technology is complicated and time-consuming in dealing with such constrained CAP (cCAP), and immune clone selection algorithm with variable neighborhood operation (ICSAVNS) is provided for solving this problem. Two approaches to initial solution generation are designed to improve the quality of the initial population. A variable neighborhood search operator is embedded to improve the accuracy of the local search. A threshold is set in the mutation operation of the ICSAVNS to achieve population expansion better. A double index of sequences consisting of affinity values and constrained facility index values is used to select and reselect, achieving population compression in the clonal selection part. Finally, by exactly solving the model, the rationality of the model is verified. The hybrid clone selection algorithm is used to solve the cCAP and cbCAP benchmark instances of different sizes, and compared with the state-of-the-art optimization algorithms. The results show that the proposed algorithm exhibits better performance.

Optimally scheduling and loading tow trains of in-plant milk-run delivery for mixed-model assembly lines

PurposeThis paper aims to investigate the scheduling and loading problems of tow trains for mixed-model assembly lines (MMALs). An in-plant milk-run delivery model has been formulated to minimize total line-side inventory for all stations over the planning horizon by specifying the departure time, parts quantity of each delivery and the destination station.Design/methodology/approachAn immune clonal selection algorithm (ICSA) combined with neighborhood search (NS) and simulated annealing (SA) operators, which is called the NSICSA algorithm, is developed, possessing the global search ability of ICSA, the ability of SA for escaping local optimum and the deep search ability of NS to get better solutions.FindingsThe modifications have overcome the deficiency of insufficient local search and deepened the search depth of the original metaheuristic. Meanwhile, good approximate solutions are obtained in small-, medium- and large-scale instances. Furthermore, inventory peaks are in control according to computational results, proving the effectiveness of the mathematical model.Research limitations/implicationsThis study works out only if there is no breakdown of tow trains. The current work contributes to the in-plant milk-run delivery scheduling for MMALs, and it can be modified to deal with similar part feeding problems.Originality/valueThe capacity limit of line-side inventory for workstations as well as no stock-outs rules are taken into account, and the scheduling and loading problems are solved satisfactorily for the part distribution of MMALs.

An improved immune clonal selection algorithm for bi-objective robotic assemble line balancing problems considering time and space constraints

PurposeThe extensive applications of the industrial robots have made the optimization of assembly lines more complicated. The purpose of this paper is to develop a balancing method of both workstation time and station area to improve the efficiency and productivity of the robotic assembly lines. A tradeoff was made between two conflicting objective functions, minimizing the number of workstations and minimizing the area of each workstation.Design/methodology/approachThis research proposes an optimal method for balancing robotic assembly lines with space consideration and reducing robot changeover and area for tools and fixtures to further minimize assembly line area and cycle time. Due to the NP-hard nature of the considered problem, an improved multi-objective immune clonal selection algorithm is proposed to solve this constrained multi-objective optimization problem, and a special coding scheme is designed for the problem. To enhance the performance of the algorithm, several strategies including elite strategy and global search are introduced.FindingsA set of instances of different problem scales are optimized and the results are compared with two other high-performing multi-objective algorithms to evaluate the efficiency and superiority of the proposed algorithm. It is found that the proposed method can efficiently solve the real-world size case of time and space robotic assembly line balancing problems.Originality/valueFor the first time in the robotic assembly line balancing problems, an assignment-based tool area and a sequence-based changeover time are took into consideration. Furthermore, a mathematical model with bi-objective functions of minimizing the number of workstations and area of each station was developed. To solve the proposed problem, an improved multi-objective immune clonal selection algorithm was proposed and a special coding scheme is designed.

A multi-objective trajectory planning method based on the improved immune clonal selection algorithm

In this paper, a multi-objective trajectory planning method based on an improved immune clonal selection algorithm is proposed. On the basis of the inverse kinematic analysis and dynamic model of the 6-UPU parallel assembly platform (PAP), the motion trajectory of the mobile platform is planned in Cartesian space with a quintic B-spline curve. Three important technical indexes, i.e. assembly efficiency, production cost and motion stationarity, are selected to evaluate the performance of planned trajectory. Correspondingly, three ideal objective functions which can represent the aforementioned technical indicators, namely the functions respectively of the total motion time, of the energy consumed in the process of motion and of the maximum absolute value of actuator's jerk, are defined to establish the multi-objective model for trajectory optimization. Then an improved immune clonal selection algorithm (IICSA) is developed to solve the optimization problem. Results of trajectory simulation confirm that the demands of assembly task can be met. Thus, the proposed method is effective and universal and it has the potential to be referenced or applied directly to other types of manipulators.

RETRACTED ARTICLE: Analyzing genetic diseases using multimedia processing techniques associative decision tree-based learning and Hopfield dynamic neural networks from medical images

Genetic diseases are the most common next-generation diseases because of the improper mutation of the genes and DNA. These genetic diseases are failed to predict with an accurate manner in the beginning stage by using the particular genes and related information. So, the genetic diseases are identified in the medical systems by utilizing the hybridization of multimedia techniques such as big data and related soft computing techniques.Initially, the genetic disease-related medical images are collected from healthcare sectors, and from the genetic image, various genetic data are collected from the large amount of datasets in which the major challenge is too high dimensionality that increases the complexity of the genetic disease prediction system. So, in this paper the complexity of the system is reduced by using the associative decision tree-based learning and Hopfield dynamic neural networks (HDNN). After collecting the data from the various resources, the immune clonal selection algorithm approach is used to remove inconsistent data and minimize the dimensionality of data. The selected features are trained by the proposed associative decision tree approach which helps to compare with the testing features using the HDNN that successfully recognize the genetic disease-based features effectively. The excellence of the system is measured with the aid of the experimental outcomes that are corresponding to the forecasting methods such as greedy algorithm, rough set method and artificial bee colony, and the comparison is made with the avail of the accuracy, sensitivity and specificity.

Task Assignment Algorithm for Road Patrol by Multiple UAVs With Multiple Bases and Rechargeable Endurance

Increasing attention has been paid to the application of Unmanned Aerial Vehicles (UAVs) in traffic field. Existing studies on UAVs in road patrol task assignment rarely integrate the actual requirements of multiple bases, charging problems during inspection and multiple types of UAVs into the patrol model, and rarely consider the fastest time to complete the patrol of all task points as the optimization target. In addition, most existing solving algorithms are difficult to use to directly solve problem models meeting the above requirements and objectives, and some existing algorithms need to be improved in terms of solving quality. In order to solve those problems, a task assignment model for road patrol by multiple UAVs with multiple bases and rechargeable endurance is established, and a time-priority immune clonal selection algorithm with optimization-revision is proposed. The optimal sequence of task points is obtained by the immune clonal selection algorithm, and the time-priority method is adopted to divide the sequence of task points. The optimal UAV paths are further optimized and modified. With 20 task points, the experimental results show that the earliest completion time obtained by the proposed algorithm decreased by 1.071 and 2.1209 compared with the earliest reachable time particle swarm optimization algorithm and the improved dynamic programming ant colony algorithm, respectively. The results indicate that proposed algorithm achieves better performance in terms of solution quality. In addition, experiments with 50 and 100 task points show that the algorithm is also suitable for medium and large scale problems.

Ensemble swarm behaviour based feature selection and support vector machine classifier for chronic kidney disease prediction

Kidney Disease and kidney failure is the one of the complicated and challenging health issues regarding human health. Without having any symptoms few diseases are detected in later stages which results in dialysis. Advanced excavating technologies can always give various possibilities to deal with the situation by determining important realations and associations in drilling down health related data. The prediction accuracy of classification algorithms depends upon appropriate Feature Selection (FS) algorithms decrease the number of features from collection of data. FS is the procedure of choosing the most relevant features, removing irrelevant features. To identify the Chronic Kidney Disease (CKD), Hybrid Wrapper and Filter based FS (HWFFS) algorithm is proposed to reduce the dimension of CKD dataset. Filter based FS algorithm is performed based on the three major functions: Information Gain (IG), Correlation Based Feature Selection (CFS) and Consistency Based Subset Evaluation (CS) algorithms respectively. Wrapper based FS algorithm is performed based on the Enhanced Immune Clonal Selection (EICS) algorithm to choose most important features from the CKD dataset. The results from these FS algorithms are combined with new HWFFS algorithm using classification threshold value. Finally Support Vector Machine (SVM) based prediction algorithm be proposed in order to predict CKD and being evaluated on the MATLAB platform. The results demonstrated with the purpose of the SVM classifier by using HWFFS algorithm provides higher prediction rate in the diagnosis of CKD when compared to other classification algorithms.

Improved hybrid immune clonal selection genetic algorithm and its application in hybrid shop scheduling

This paper is based on the multi-objective optimization problem of mixed shop scheduling problem, the strong coupling of the maximum flow and the minimum time, and the deficiencies of the immune genetic algorithm including high computational complexity and high spatial dimension. This paper establishes a mixed shop scheduling mathematical model with the minimization of the maximum total completion time as the target, and puts forward to use the immune clonal selection algorithm to solve the problem. In the algorithm population construction, it uses the grouping strategy, introduces the cross and delete operator, retains the excellent individuals through memory space, deletes the relatively bad individual, and improves the algorithm’s global optimization ability. In order to verify the effectiveness of the proposed algorithm, under the two experimental environments of workpiece machining and automobile shock absorber processing workshop scheduling, simulation experiments are conducted. The experimental results show that the proposed algorithm has better performance, and can achieve smaller maximum total completion time with less iteration. The algorithm can find the global optimal solution of the multi-objective problem, which has a strong practical significance.

A study on car flow organization in the loading end of heavy haul railway based on immune clonal selection algorithm

Both empty and heavy car flows should be taken into consideration in the study of the Optimization Model of Car Flow Organization, in the Loading End of Heavy Haul Railway. Firstly, through the analysis of empty and heavy car flow organization in the semi-closed heavy haul railway loading end, comprehensive optimization model based on ability of station and railway can be established in order to obtain car flow organization scheme with minimum residence time of both empty and heavy cars in the loading end. Secondly, immune clone algorithm is applied to solve the model in which the objective function is regarded as antibody and the constrained condition is considered as antigen to calculate the affinity, further to conduct the mutation, based on information entropy. Mutation is conducted according to the affinity. In the solving process, reproduction can be realized with the use of immune clone selection algorithm. Besides, premature convergence can be prevented by using the antibody concentration which can control the population size. Finally, simulation results show that the average search time of the proposed algorithm is reduced by 36 and 51% respectively compared with the GA and PSO.

Application of Multi-Step Parameter Estimation Method Based on Optimization Algorithm in Sacramento Model

The Sacramento model is widely utilized in hydrological forecast, of which the accuracy and performance are primarily determined by the model parameters, indicating the key role of parameter estimation. This paper presents a multi-step parameter estimation method, which divides the parameter estimation of Sacramento model into three steps and realizes optimization step by step. We firstly use the immune clonal selection algorithm (ICSA) to solve the non-liner objective function of parameter estimation, and compare the parameter calibration result of ideal artificial data with Shuffled Complex Evolution (SCE-UA), Parallel Genetic Algorithm (PGA), and Serial Master-slaver Swarms Shuffling Evolution Algorithm Based on Particle Swarms Optimization (SMSE-PSO). The comparison result shows that ICSA has the best convergence, efficiency and precision. Then we apply ICSA to the parameter estimation of single-step and multi-step Sacramento model and simulate 32 floods based on application examples of Dongyang and Tantou river basins for validation. It is clearly shown that the results of multi-step method based on ICSA show higher accuracy and 100% qualified rate, indicating its higher precision and reliability, which has great potential to improve Sacramento model and hydrological forecast.

Studies on Immune Clonal Selection Algorithm and Application of Bioinformatics

Immune algorithms (IAs) are microscopic view of evolutionary algorithms (EAs) and applied in combi- natorial optimization problems. This paper addresses to a clonal selection algorithm (CSA) that is one of the most representative IA and was applied into the protein structure prediction (PSP) on AB off-lattice model, in which the memory B cells of the CSA was innovated by employing different strategies: local search and global search in the phase of the mutation. And the CSA was further improved by adding aging operator to combat the premature con- vergence. However the pure aging operator didn't achieve effective results and sometimes the optimum solution was eliminated. To resolve this problem, the current best solution was reserved by an antibody and it was not eliminated when its age reached its life span. In our experiments the improved algorithm was compared with the standard CSA and the pure aging CSA, which of the results demonstrated that the improved strategy with the memory B cells and long life aging was very effective to overcome premature convergence and to avoid trapped in the local-best solution, and it was also effective in keeping the diversity of the small size population. On the other hand, one novel hybrid algorithm Quantum Immune(QI), which combines Quantum Algorithm (QA) and Immune Clonal Selection(ICS) Al- gorithm, has been presented for dealing with multi-extremum and multi-parameter problem based on AB off-lattice model in the predicting 2D protein folding structure. Clonal Selection Algorithm was introduced into the hypermuta- tion operators in the Quantum Algorithm to improve the local search ability, and double chains quantum coded was designed to enlarge the probability of the global optimization solution. It showed that the solution mostly trap into the local optimum, to escape the local best solution the aging operator is introduced to improve the performance of the algorithm. Experimental results showed that the lowest energies and computing-time of the improved Quantum Clonal Selection(QCS) algorithm were better than that of the previous methods, and the QCS was further improved by adding aging operator to combat the premature convergence. Compared with previous approaches, the improved QCS algorithm remarkably enhanced the convergence performance and the search efficiency of the immune optimization algorithm.

Immune clonal selection algorithm for capacitated arc routing problem

In existing metaheuristics for solving the capacitated arc routing problem, traversal local search operators are often used to explore neighbors of the current solutions. This mechanism is beneficial for finding high-quality solutions; however, it entails a large number of function evaluations, causing high computational complexity. Hence, there is a need to further enhance the efficiency of such algorithms. This paper proposes a high-efficiency immune clonal selection algorithm for capacitated arc routing instances within a limited number of function evaluations. First, an improved constructive heuristic is used to initialize the antibody population. The initial antibodies generated by this heuristic help accelerate the algorithm's convergence. Second, we show how an immune clonal selection algorithm can select in favor of these high-quality antibodies. By adopting a variety of different strategies for different clones of the same antibody, it not only promotes cooperation and information exchanging among antibodies, but also increases diversity and speeds up convergence. Third, two different antibody repair operations are proposed for repairing various kinds of infeasible solutions. These operations cause infeasible solutions to move towards global optima. Experimental studies demonstrate improved performance over state-of-art algorithms, especially on medium-scale instances.

Reactive Power Optimization Based on Immune Clonal Selection Algorithm

Based on the full understanding of the current status of the reactive power optimization study, we propose an improved type of immune algorithm to solve the reactive power optimization problem by introducing the immune clonal selection algorithm (ICSA) genetic manipulation, affinity of mature, cloning and memory mechanism, and use the appropriate operator to ensure that the algorithm can quickly converge to the global optimal solution to improve the efficiency of the algorithm solving and solution accuracy, avoiding the "curse of dimensionality" and precocious problems. ICSA algorithm is proposed to improve the convergence speed simultaneously. Better maintain the diversity of the population. Effectively overcome the premature convergence of evolutionary computation itself is difficult to solve the problem. Four different examples of calculation results show that this method has superior computational efficiency and convergence capability, high quality and are solved, very suitable for solving large-scale power system reactive power optimization problem, with a strong practical value.

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.

An Immune Clonal Selection Algorithm for Synthetic Signature Generation

The collection of signature data for system development and evaluation generally requires significant time and effort. To overcome this problem, this paper proposes a detector generation based clonal selection algorithm for synthetic signature set generation. The goal of synthetic signature generation is to improve the performance of signature verification by providing more training samples. Our method uses the clonal selection algorithm to maintain the diversity of the overall set and avoid sparse feature distribution. The algorithm firstly generates detectors with a segmentedr-continuous bits matching rule andP-receptor editing strategy to provide a more wider search space. Then the clonal selection algorithm is used to expand and optimize the overall signature set. We demonstrate the effectiveness of our clonal selection algorithm, and the experiments show that adding the synthetic training samples can improve the performance of signature verification.

Improved Immune Clonal Selection Algorithm for Photovoltaic Maximum Power Point Tracking Control

Improved Immune Clonal Selection Algorithm for Photovoltaic Maximum Power Point Tracking Control

A synergetic immune clonal selection algorithm based multi-objective optimization method for carbon fiber drawing process

Carbon fiber production is a large-scale system which comprises a large number of production processes. Among the various complex production conditions, the drawing process is one of the most influential factors that affect the quality of carbon fiber. How to obtain the fittest process parameters of the drawing process is a typical multi-objective optimization problem. To address the drawbacks of mathematical programming techniques available for solving optimization problems, we propose a new synergetic immune clonal selection algorithm (SICSA) to obtain the optimal process parameters, such as the linear density, strength, and breaking elongation ratio. The main operators of the SICSA are synergetic evolution, clonal operation and non-uniform mutation. The synergetic evolution between populations adopts a “division-parallel-recombination” mode, the clonal operation searches for optimal solutions globally, and the non-uniform mutation explores optimal solutions locally and enhances the diversity of the solutions. As a result, optimal solutions which lead to reasonable distribution of the drawing ratio are obtained. We also compare the proposed SICSA with an immune algorithm and a genetic algorithm for optimizing the parameter in the drawing process. Our results show that the SICSA has the best performance in precision and convergence time. These results can serve as references and provide guidance for real production of carbon fiber.