Multi-objective Immune Algorithm Research Articles

Q-learning based multi-objective immune algorithm for fuzzy flexible job shop scheduling problem considering dynamic disruptions

Confronted with complex industrial environments, dynamic disruptions like new job arrival and machine breakdown bring significant challenges to the robustness and stability of the manufacturing process, making the static production depart from the original scheduling scheme. To address this problem, a flexible job shop scheduling problem with fuzzy processing time, dynamic disruptions, and variable processing speeds is considered simultaneously. As well as three objectives of maximum completion time, total energy consumption, and average agreement index are demonstrated in this study. Then, a predictive-reactive dynamic/static rescheduling model is developed, where the off-line based mixed integer linear programming model and the on-line based rescheduling heuristics are proposed. Next, a multi-objective immune algorithm combined with a Q-learning algorithm (Q-MOIA) is developed. In the proposed algorithm, an active decoding heuristic based on the interval insertion mechanism is used to optimize the initial solutions. After that, the clonal selection-based immune algorithm and the Q-learning algorithm are adopted to improve the exploration and exploitation capabilities, respectively, where four objective-driven neighborhood structures are designed. Eventually, extensive computational experiments were conducted on 27 instances under static and dynamic scenarios to demonstrate the superiority and stability of the proposed predictive-reactive dynamic/static rescheduling model and the Q-MOIA. Comparative analysis with four state-of-the-art approaches revealed that proposed Q-MOIA outperformed in approximately 51.9, 66.7, and 83.3 % of the instances for the three multi-objective metrics.

Techno-economic and environmental benefits-oriented human–robot collaborative disassembly line balancing optimization in remanufacturing

The remanufacturing process, driven by human–robot collaboration technology, is becoming an important carrier for the circular economy, contributing to economic development while significantly reducing environmental pressure. However, existing researches on human–robot collaboration disassembly line have certain limitations and fails to address economic and environmental considerations adequately. Therefore, in this study, a techno-economic and environmental benefit-oriented human–robot collaborative disassembly line balancing problem (TEBHRC-DLBP) was formulated that requires the utilization of human and robot resources to improve disassembly efficiency and quality and to facilitate decision-making on recycling options for disassembled parts to maximize economic and environmental benefits. First, a mixed-integer programming (MIP) model was developed for the TEBHRC-DLBP to minimize the number of workstations, minimize the smoothing index, and maximize techno-economic and environmental benefits. Second, a multi-objective immune genetic algorithm (MIGA) was developed to solve the proposed TEBHRC-DLBP efficiently. A five-layer encoding method and a triple decoding strategy were constructed based on the problem characteristics, and immune operators were introduced into the well-known NSGA-II structure to interfere with the global search process with a certain degree of strength to avoid invalid work and to improve algorithm efficiency. In addition, the correctness and validity of the proposed MIP model and the MIGA were verified using the case of a small-scale personal computer. In 21 benchmark tests with scales ranging from 7 to 148, the proposed MIGA achieved significantly better results than the seven algorithms reported in the literature and obtained the best value for 16 benchmarks. Finally, the application of the MIGA to the disassembly case of a power battery module demonstrates its good stability, convergence, and diversity, as well as its excellent practical application ability. In particular, the proposed method can change the disassembly scheme according to different disassembly planning periods to maximize economic and environmental benefits.

A novel multi-objective immunization algorithm based on dynamic variation distance

Multi-objective immunization algorithms have received wide attention in solving multi-objective optimization problems. However, most multi-objective immune algorithms use clone selection strategies to accelerate convergence and focus on only the local information of clone solutions, which damages the diversity of the population and leads the algorithms to fall into local optimum easily. To address the above problems, this paper proposes a novel multi-objective immunization algorithm based on dynamic variation distance. First, the algorithm ensures the uniform distribution of the optimal solution by the established dynamic variation distance model. Second, we divide the population into mainpop and auxpop by quality, and design a new evolutionary strategy to achieve specific variants at different evolutionary stages under different constraints. Third, the mating selection strategy and the population selection operator are further explored to handle infeasible solutions adaptively in the late evolutionary stage. And we have conducted extensive experiments on the proposed algorithm. The results prove that our proposed algorithm outperforms 14 existing algorithms in most cases of 16 test problems, which confirms the superiority of the algorithm.

Multi-Objective Immune Optimization of Path Planning for Ship Welding Robot

In order to improve the welding efficiency of the ship welding robot, the path planning of the welding robot based on immune optimization is proposed by taking the welding path length and energy loss as the optimization goals. First, on the basis of the definition of the path planning of the welding robot, the grid modeling of the robot’s working environment and the triangular modeling of the welding weldments are carried out. Then, according to the working process of the welding robot, the length objective function, including the welded seam path and the welding torch path without welding, is constructed, and the energy loss function is constructed based on the kinematics and Lagrange function. Finally, the immune optimization algorithm based on cluster analysis and self-circulation is introduced to realize the multi-objective optimization of the path planning for the ship welding robot. The test results of four kinds of ship welding weldments show that compared with the simple genetic algorithm, immune genetic algorithm, ant colony algorithm, artificial bee colony, particle swarm optimization, and immune cloning optimization, the proposed multi-objective immune planning algorithm is the best in terms of planning path length, energy consumption, and stability. Furthermore, the average shortest path and its standard deviation, the average minimum energy consumption and its standard deviation, and the average lowest convergence generation and its standard deviation are reduced by an average of 9.03%, 54.04%, 8.23%, 19.10%, 27.84%, and 52.25%, respectively, which fully verifies the effectiveness and superiority of the proposed welding robot path planning algorithm.

Symmetric uncertainty based decomposition multi-objective immune algorithm for feature selection

Symmetric uncertainty based decomposition multi-objective immune algorithm for feature selection

Solving the Vehicle Routing Problem for a Reverse Logistics Hybrid Fleet Considering Real-Time Road Conditions

In this study, a bi-objective optimization model was established to solve the cooperative distribution problem of a multi-center hybrid fleet by integrating reverse logistics under real-time road conditions. According to the characteristics of the problem and considering the power level and battery capacity of electric vehicles, the multi-objective immune genetic algorithm (MOIGA) was designed and compared with an elitist strategy genetic algorithm, i.e., the fast non-dominated sorting genetic algorithm (NSGA-II). The scale of the MOIGA solution set exceeded that of the NSGA-II, which proved that the global search ability of MOIGA was better than that of the NSGA-II. The operating efficiency of the MOIGA was lower than that of the NSGA-II, but it could also find the optimal solution within an acceptable time range. This method can reduce the total cost of operating a hybrid fleet and can meet the needs of customers, and therefore, improve customer satisfaction.

Multiple objective immune wolf colony algorithm for solving time-cost-quality trade-off problem.

The importance of the time-cost-quality trade-off problem in construction projects has been widely recognized. Its goal is to minimize time and cost and maximize quality. In this paper, the bonus-penalty mechanism is introduced to improve the traditional time-cost model, and considering the nonlinear relationship between quality and time, a nonlinear time-cost quality model is established. Meanwhile, in order to better solve the time-cost-quality trade-off problem, a multi-objective immune wolf colony optimization algorithm has been proposed. The hybrid method combines the fast convergence of the wolf colony algorithm and the excellent diversity of the immune algorithm to improve the accuracy of the wolf colony search process. Finally, a railway construction project is taken as an example to prove the effectiveness of the method.

Mobile edge computing offloading scheme based on improved multi-objective immune cloning algorithm

Mobile edge computing offloading scheme based on improved multi-objective immune cloning algorithm

A survey of artificial immune algorithms for multi-objective optimization

Multi-objective immune algorithm (MOIA) is a heuristic algorithm based on artificial immune system model. Due to its characteristics of antibody clonal selection, automatic antigen recognition and immune memory in the immune system, artificial immune algorithm has become a research hotspot in the field of multi-objective optimization after the evolutionary algorithms. In this paper, most MOIAs can be classified into three main categories according to the type of problem solving, i.e., they are mostly designed to solve multi-objective optimization problems (MOPs), dynamic MOPs, and constrained MOPs. In this paper, a comprehensive survey is presented to summarize most existing MOIAs, in which their corresponding characteristics, principles and theoretical analyses are discussed in details. Moreover, the performance of MOIAs on solving various kinds of MOPs and many-objective optimization problems is also studied in our experimental comparisons. Finally, a brief conclusion is given to summarize the current drawbacks, challenges, and some future directions for MOIAs.

Many‐objective optimization by using an immune algorithm

Multiobjective optimization is important in practical engineering applications. With the increased number of objectives, multiobjective optimization becomes more challenging due to the difficulty of convergence in population selection. A number of many-objective evolutionary algorithms (MaOEAs) have been designed to enhance population selection, but studies selecting parents for evolution are still rare. Fortunately, multiobjective immune algorithms (MOIAs) provide a promising approach to select high-quality parents for evolution. However, the existing MOIAs are not effective for solving many-objective optimization problems (MaOPs), as these algorithms consider only the local information of solutions for cloning but ignore the global information of populations; consequently, the populations of these algorithms may easily be trapped in local optima. To solve this problem, this paper proposes a many-objective immune algorithm with a novel immune cloning operator. In this approach, the global information in the population is used to estimate the quality of each solution, and only a few offspring from high-quality parents are generated in each generation to improve the convergence and diversity of the population. When the proposed algorithm is compared with nine MaOEAs and six MOIAs on three MaOP benchmarks with 5, 10, and 15 objectives, the experimental results validate that the proposed algorithm obtains the best performance in most cases. Moreover, the effectiveness of the proposed algorithm is also validated on one real-world optimization problem.

Retraction Note to: A fuzzy multi-objective immune genetic algorithm for the strategic location planning problem

Retraction Note to: A fuzzy multi-objective immune genetic algorithm for the strategic location planning problem

Multi-Objective Immune Algorithm for Internet of Vehicles for Data Offloading

On the Internet of Vehicle (IoV) devices, offloading data is the major problem because massive amounts of data generate energy consumption, and the execution cost is high. At present, accidents traffic management is highly prominent due to increased vehicles among the population. IoV is the only technology to help the transport system effectively. This data outreach the memory also has high energy consumption, and the storage cost is high. To overcome these issues, a Mobility aware Offloading scheme with Multi-Objective Immune Optimization algorithm (MOS-MOIO) is used in the cloud storage. The data is generated from the online sensor system. The generated information must alert the IoV users with a useful data offloading scheme. The result of the data offloading is evaluated with various performance metrics.

Analysis of Energy Efficient Scheduling of the Manufacturing Line with Finite Buffer Capacity and Machine Setup and Shutdown Times

The aim of this paper is to present a model of energy efficient scheduling for series production systems during operation, including setup and shutdown activities. The flow shop system together with setup, shutdown times and energy consumption are considered. Production tasks enter the system with exponentially distributed interarrival times and are carried out according to the times assumed as predefined. Tasks arriving from one waiting queue are handled in the order set by the Multi Objective Immune Algorithm. Tasks are stored in a finite-capacity buffer if machines are busy, or setup activities are being performed. Whenever a production system is idle, machines are stopped according to shutdown times in order to save energy. A machine requires setup time before executing the first batch of jobs after the idle time. Scientists agree that turning off an idle machine is a common measure that is appropriate for all types of workshops, but usually requires more steps, such as setup and shutdown. Literature analysis shows that there is a research gap regarding multi-objective algorithms, as minimizing energy consumption is not the only factor affecting the total manufacturing cost—there are other factors, such as late delivery cost or early delivery cost with additional storage cost, which make the optimization of the total cost of the production process more complicated. Another goal is to develop previous scheduling algorithms and research framework for energy efficient scheduling. The impact of the input data on the production system performance and energy consumption for series production is investigated in serial, parallel or serial–parallel flows. Parallel flow of upcoming tasks achieves minimum values of makespan criterion. Serial and serial–parallel flows of arriving tasks ensure minimum cost of energy consumption. Parallel flow of arriving tasks ensures minimum values of the costs of tardiness or premature execution. Parallel flow or serial–parallel flow of incoming tasks allows one to implement schedules with tasks that are not delayed.

A decomposition-based multi-objective immune algorithm for feature selection in learning to rank

Learning-to-rank (L2R) based on feature selection has been proved effectively. However, feature selection problem is more challenging due to two conflicting objectives, namely, maximizing classification performance and minimizing the number of features. Therefore, in this article, a novel decomposition-based multi-objective immune algorithm for feature selection in L2R, called MOIA/D-FSRank, is proposed. The proposed algorithm associates each solution with a scalar subproblem based on Tchebycheff decomposition approach, which makes the optimization process more efficient. In addition, we propose an Elite Selection Strategy (ESS) in initialization phase, which can significantly improve the diversity and convergence of the initial population. Moreover, the proposed algorithm uses two effective operators, a clonal selection operator and a mutation operator, where the clonal selection operator is to generate clone population to better guide the search direction of the evolution and the mutation operator aims to retain excellent features with a higher probability in evolution. In this paper, the pairwise method with O(n2) size is used to train the ranking model, and extensive experiments are conducted on the four public LETOR benchmark data sets. The experimental results demonstrate that the proposed algorithm can obtain significant performances on the ranking accuracy and the number of features.

Dynamic immune cooperative scheduling of agricultural machineries

Considering the low flexibility and efficiency of the scheduling problem, an improved multi-objective immune algorithm with non-dominated neighbor-based selection and Tabu search (NNITSA) is proposed. A novel Tabu search algorithm (TSA)-based operator is introduced in both the local search and mutation stage, which improves the climbing performance of the NNTSA. Special local search strategies can prevent the algorithm from being caught in the optimal solution. In addition, considering the time costs of the TSA, an adapted mutation strategy is proposed to operate the TSA mutation according to the scale of Pareto solutions. Random mutations may be applied to other conditions. Then, a robust evaluation is adopted to choose an appropriate solution from the obtained Pareto solutions set. NNITSA is used to solve the problems of static partitioning optimization and dynamic cross-regional co-operative scheduling of agricultural machinery. The simulation results show that NNITSA outperforms the other two algorithms, NNIA and NSGA-II. The performance indicator C-metric also shows significant improvements in the efficiency of optimizing search.

An effective integrated control with intelligent optimization for wastewater treatment process

Wastewater treatment process (WWTPs) is an enormous and complex system, it involves multi-disciplines and multi-fields, which is a greatly challenge for industrial Information Integration Engineering (IIIE). In this paper, an integrated control framework for WWTPs is proposed using intelligent optimization to solve the problem of excessive pollutant and high energy consumption. It realizes the effective integration of data model, intelligent decision making, dynamic optimization and control. Firstly, a self-organization soft sensing method is adopted. It can predict the concentrations of ammonia nitrogen and total nitrogen in advance. And the early warning is provided for decision making. Secondly, for the complex dynamic characteristics of multi-performance indexes intercoupling in WWTPs, the dynamic multi-objective immune optimization algorithm is designed. And an optimized set point with high quality in real time is to be obtained. In addition, according to different over standard states, the intelligent decision scheme of WWTPs is designed. It can achieve the effective cooperation of over standard suppression and energy saving. Finally, BSM1 simulation model is used to verify the method. The experimental results show that the integrated framework can achieve the whole process of WWTPs real-time standards and can effectively reduce energy consumption, so it has competitive practical application prospect for IIIE.

Vertical distance-based clonal selection mechanism for the multiobjective immune algorithm

Traditional multiobjective immune algorithms (MOIAs) widely use the domination relationship and crowding distance metric to run the cloning operator, which places more attention on nondominated solutions with larger crowding distance values. This “convergence-first” cloning principle may hamper population diversity. To better maintain population diversity in most traditional MOIAs, we propose a novel vertical distance-based clonal selection mechanism for MOIAs, called VD-MOIA in this paper, which arranges the cloning numbers for each solution based on the vertical distance value between the solution and its corresponding weight vector. First, the decomposition approach is used to decompose the target MOP into a set of subproblems, which are then optimized cooperatively and simultaneously. Second, the vertical distance-based clonal selection mechanism is performed, which puts more emphasis on the solutions with small vertical distance values. A large number of clones is allocated to the solutions that show good performance in terms of diversity, as these solutions are closer to their corresponding weight vectors than solutions that result in poor diversity. Moreover, the aggregated values quantified by the Chebyshev function are used to ensure convergence, while differential evolution (DE) is employed to strengthen the exploration ability of the algorithm, which further improves the population diversity as well. To validate the effectiveness of our proposed algorithm, twenty-eight multiobjective benchmark problems are adopted with complicated Pareto-optimal sets and fronts. The experimental comparison results validate the superiority of our proposed VD-MOIA when compared with four competitive MOIAs and four state-of-the-art multiobjective evolutionary algorithms.

Research on Performance Optimization of Electric Vehicle Based on Multi-objective Immune Algorithm

Aiming at the shortage of power and economy of a pure electric vehicle, the multi-objective immune algorithm is adopted to optimize the power index and the economy index with the maximum speed, 0 ∼ 50km / h and 50 ∼ 80km / h acceleration time and climbing slope as the power index and the endurance mileage as the economy index. In this paper, the main reducer ratio, the first gear ratio and the second gear ratio of the transmission are taken as variables, the dynamic and economic equations are taken as objective functions, and the multi-objective immune algorithm is used to obtain the three variables and other parameters satisfying the optimization of the function. Through the use of pure electric vehicle simulation software ADVISOR to input the optimized matching parameters, select 0∼80km/h and NEDC working conditions, and set the climbing and acceleration parameters, the simulation results show that: the maximum speed is increased by 22%, the acceleration time is increased by 5.8%, 21.4%, the climbing slope is increased by 22%, and the endurance mileage is increased by 3.1%.

Joint Nodes and Sink Mobility Based Immune Routing-Clustering Protocol for Wireless Sensor Networks

Recently, mobile wireless sensor network has drawn attention widely. In this paper, Joint Nodes and Sink Mobility based Immune routing-Clustering protocol (JNSMIC) is proposed to support the mobility of the sink and the sensor nodes together. It depends on using the mobile sink for solving the hot spot problem and the Multi-Objective Immune Algorithm (MOIA) for clustering the network and finding the visiting locations of the mobile sink. The JNSMIC protocol considers different objectives during the clustering process, namely the consumption energy, network coverage, link connection time (LCT), residual energy and mobility. Also, it reduces the computational time of finding cluster heads (CHs) by dividing it into two phases. In the first phase, the candidate CHs set is formed based on residual energy, mobility factor and LCT of sensor nodes. While in the second phase, the MOIA algorithm is utilized to determine the final CHs subject to reducing the communication cost, improving the packet delivery ratio and ensuring network stability. JNSMIC performs the clustering process only if the remaining energy is below a threshold value thus the computational time and overhead control packets are reduced. In JNSMIC, the deputy CH concept is considered to perform the task of CH during CH failure. Furthermore, the proposed protocol performs a fault-tolerance process after transmitting each frame to maintain the link stability among CHs and their members which improves the throughput. Simulation results show that the JNSMIC protocol can effectively ameliorate the throughput while simultaneously giving lower energy expenditure and end-to-end delay.

An MRI-based multi-objective radiomics model predicts lymph node status in patients with rectal cancer.

To apply a multi-objective radiomics model based on pre-operative magnetic resonance imaging (MRI) for improving diagnostic accuracy of LN metastasis in rectal cancer patients. This study consisted of 91 patients diagnosed with rectal cancer from April 2018 to March 2019. All patients underwent rectal MRI before surgery without any other treatment. Clinical data, subjective radiologist assessments, and radiomic features of LNs were obtained. A total of 1409 radiomic features were extracted from T2WI LN images. Multi-objective optimization with the iterative multi-objective immune algorithm (IMIA) was used to select radiomic features to build prediction models. Predictive performances of radiomic, radiologist, and combined radiomic and radiologist models were assessed for accuracy by receiver operating characteristics (ROC) curves. For the radiologist analysis, heterogeneity was the only significant independent predictor of LN status. The sensitivity, specificity, and accuracy of the subjective radiologist analysis were 72.09%, 73.81%, and 78.12%, respectively. The sensitivity, specificity, and accuracy of the solitary radiomic model consisting of 10 features were 89.81%, 82.57%, and 87.77%, respectively. The sensitivity, specificity, and accuracy of the combined model, which consisted of 12 radiomic and radiologist features, were 92.23%, 84.69%, and 89.88%, respectively. The combined model had the best prediction performance with an AUC of 0.94. The multi-objective radiomics model based on T2WI images was very useful in predicting pre-operative LN status in rectal cancer patients.