Nondominated Neighbor Immune Algorithm Research Articles

Integrated scheduling of multi-site repair tasks considering customer preferences under a novel two-dimensional combination warranty strategy

Current warranty strategies typically focus on the interests of either the manufacturer or the customer, and fail to meet the diverse needs of the customers, as they only consider a single reimbursement or repair method. Furthermore, these strategies neglect out-of-warranty customers, resulting in a loss of profit for manufacturers. To serve both in-warranty and out-of-warranty customers, a novel two-dimensional combination warranty strategy is proposed in this paper which combines two reimbursement methods and two repair methods. Under the two-dimensional combination warranty strategy, we establish an integrated scheduling model for determining repair strategies and allocating service resources, referred to as 2DCW-MSRASM. In 2DCW-MSRASM, the manufacturer arranges appropriate warranty strategies for in-warranty orders according to the corresponding product conditions (age and usage), while for out-of-warranty customers, the manufacturer makes decisions based on its own resource situation and customer preferences. The objective of 2DCW-MSRASM is to achieve an optimal scheduling that maximizes the manufacturer’s profit and the satisfaction of both in-warranty and out-of-warranty customers. In this model, TODIM (an acronym in Portuguese of Interactive and Multi-Criteria Decision Making) is utilized to evaluate the repair services from the customer’s perspective, which considers the limited rationality of decision-makers. An improved non-dominated neighbor immune algorithm (INNIA) is developed to solve the model, incorporating an initialization method and three problem-oriented local search operators to enhance search capability. Following that, extensive experiments are conducted to demonstrate the superiority of INNIA. Eventually, the proposed strategy is demonstrated to possess noteworthy advantages when the order size is large and the proportion of in-warranty orders is high.

A New Global Gas Dynamics Performance Optimization Method for Refrigeration Centrifugal Compressor Stage Based on the Immune Algorithm

Abstract This study proposes a new global gas dynamics optimization method, which was applied to a multi-objective optimization task of centrifugal compressor performance with the aim of determining the improvement probability for achieving high efficiency across a wide operating range. Initially, the original nondominated neighbor immune algorithm (NNIA) was extended to solve constrained multi-objective optimization problems for the first time, which mainly incorporated a procedure for handling inequality and equality constraints without additional parameters. Subsequently, an adaptive topological back-propagation multilayer feed-forward artificial neural network (BP-MLFANN) was trained using the modified NNIA to quickly evaluate the fitness value of the centrifugal compressor stage performance during the optimization. The feasibility of the method was validated using the first stage of a refrigeration centrifugal compressor. The results indicated a substantial enhancement in the stage efficiency of the optimized impeller at the Near-stall, Design, and Near-choke operating points, with increasement of 1.8%, 1.9%, and 4%, respectively, as compared to the baseline stage. The flow field analysis shows that the impact loss at impeller leading edge and flow separation in the passage reduced greatly, the mixing process between the leakage flow and mainstream in the channel is significantly weakened, thus the flow field becomes more uniform after optimization. The new global gas dynamics optimization method provides a reference for the development of efficient and rapid optimization techniques for centrifugal compressor.

Integrated decision-making for repair order acceptance and service resource allocation that distinguishes between in-warranty and out-of-warranty

Manufacturers classify customers into in-warranty and out-of-warranty customers based on their warranty status at the time of product failure. However, previous studies on the decision-making optimization of orders and service resources for repair services ignored the differences between in-warranty and out-of-warranty customers, which will affect the implementation of manufacturers’ repair service businesses. Therefore, this paper proposes an integrated decision-making model for order acceptance and resource allocation for repair services that distinguishes between in-warranty and out-of-warranty and considers the manufacturer’s limited service resources. Considering the conflicts of interest between the manufacturer, in-warranty and out-of-warranty customers, the model aims to maximize the satisfaction of in-warranty and out-of-warranty customers and the manufacturer’s profit. According to the expectations of different customers and the benefits of the manufacturer, the optimal decision scheme can be derived by determining the accepted orders, adjusting the resource allocation scheme and the service sequence of the accepted orders. In addition, to obtain the optimal decision scheme more efficiently, a memetic algorithm based on the nondominated neighbor immune algorithm is proposed, in which two local search operators are designed to improve the search efficiency. Three hundred instances were used to test this model, and extensive experimental results show that the proposed algorithm is more effective than other widely used algorithms. Finally, the benefits that the proposed integrated decision-making model can bring to manufacturers are demonstrated through numerical experiments.

Optimal dispatching scheme of multi-objective cascade reservoirs by parallel mechanism-optimization algorithms

• Develop multi-objective optimal operation models for cascade reservoirs. • Hybrids Parallel Mechanism with optimization algorithms to optimize multi-objective. • Water supply contradiction and transformation rules among three objectives are obtained. • Optimal scheme ensures power generation efficiency, irrigation requirements and ecological safety. In recent years, the water conflict between competing users in the middle and lower reaches of the Heihe River has intensified, causing severe social, economic, and ecological problems. This research aims to alleviate water conflicts between multiple objectives that consider eight cascade reservoirs in the upper reaches of the Heihe River Basin to be the regulatory bodies using the optimal operation model and fuzzy optimization evaluation model. The innovative approach combining the parallel mechanism with optimization algorithms (i.e., Flower Pollination Algorithm and Non-dominated Neighbor Immune Algorithm) is proposed to solve the optimal operation models, analyze the time distribution laws of water supply dilemma and the transformation rules among three objectives (power generation, irrigation, and ecology). Based on the evaluation indicator system and subjective and objective comprehensive weighting methods (i.e., Analytic Hierarchy Process and Entropy Weight Method), a fuzzy optimization evaluation model is applied to search for the optimal dispatching scheme for the multi-objective joint operation of cascade reservoirs. Results indicate several findings. First, the Parallel - Flower Pollination Algorithm produces high precision and stability with outstanding calculation efficiency. Second, the period of the water supply dilemma is October and next April, and the annual water supply dilemma is affected by the frequency of incoming water. Third, the competitive relationship between irrigation and ecology is the strongest, followed by the relationship between ecology and power generation, the relationship between ecology and power generation is relatively weak. Fourth, the optimal dispatching scheme in each typical year is the scheme with the ratio values of the transformation coefficients among power generation, irrigation, and ecological objectives the closest to 1:1:1, respectively. It is conducive to scientifically guiding the dispatch of water resources in the Heihe River Basin, alleviating water conflicts, and ensuring socio-economic development and ecological safety.

Collaborative scheduling of spare parts production and service workers driven by distributed maintenance demand

We analyse the joint scheduling of spare parts production and service workers driven by distributed maintenance demand. For each failure, a service worker and a necessary spare part must be assigned for on-site maintenance. In particular, the spare part is not supplied from stock, but is delivered directly from the factory to the maintenance site through just-in-time production. Spare parts production and worker allocation are integrated to optimize two objectives: customer satisfaction and total service cost. To solve the problem, we first construct a mathematical model. Subsequently, a modified non-dominated neighbor immune algorithm (MNNIA) is proposed, in which a knowledge-based initialization rule, an idle time insertion method, and two problem-specific local search operators are developed to enhance exploitation. Extensive experiments and comparisons are conducted to demonstrate the superiority of MNNIA. Furthermore, compared with that of the separate decision-making mode of spare parts production and worker arrangement, the effectiveness of the integrated scheduling mode is verified.

Multi-objective trajectory planning for 5-DOF underactuated tower cranes with state constraints

In the practical applications of tower cranes, to improve work efficiency, it is necessary to simultaneously rotate the jib, move the trolley, and change the rope length during transportation; however, there exist few control methods for such 5-DOF tower crane systems. In addition, it is still open how to find a balance among reducing the transportation time, saving the energy consumption, and guaranteeing satisfactory transient performance at the same time. Therefore, to address the above issues, in this paper, a multi-objective trajectory planning method with state constraints is proposed for 5-DOF tower cranes, which is the firstsolution that can achieve Pareto optimality of the transportation time and energy consumption, and can simultaneously satisfy all the practical constraints of the state variables and their corresponding velocities. The positioning and anti-swing performance of the planned trajectories is realized by constructing differential flat outputs and designing B-spline curves. The multi-objective optimization problem is solved by the improved nondominated neighbor immune algorithm (NNIA). Finally, hardware experiments are carried out for effectiveness verification.

Analysis and Optimization of the Machining Characteristics of High-Volume Content SiCp/Al Composite in Wire Electrical Discharge Machining

With the properties of high specific strength, small thermal expansion and good abrasive resistance, the particle-reinforced aluminum matrix composite is widely used in the fields of aerospace, automobile and electronic communications, etc. However, the cutting performance of the particle-reinforced aluminum matrix composite is very poor due to severe tool wear and low machining efficiency. Wire electrical discharge machining has been proven to be a good machining method for conductive material with any hardness. Even so, the high-volume SiCp/Al content composite is still a difficult-to-machine material in wire electrical discharge machining due to the influence of insulative the SiC particle. The goal of this paper is to analyze the machining characteristics and find the optimal process parameters for the high-volume content (65 vol.%) SiCp/Al composite in wire electrical discharge machining. Experimental results show that the material removal method of the SiCp/Al composite includes sublimating, decomposing and particle shedding. The material removal rate is found to increase with the increasing pulse-on time, first increasing and then decreasing with the increasing pulse-off time, servo voltage, wire feed and wire tension. Pulse-on time and servo voltage are the dominant factors for surface roughness. In addition, the multi-objective optimization method of the nondominated neighbor immune algorithm is presented to optimize the process parameters for a fast material removal rate and low surface roughness. The optimized process parameters can increase the material removal rate by 34% and reduce the surface roughness by 6%. Furthermore, the effectiveness of the Pareto optimal solution is proven by the verified experiment.

System Reliability Optimization of Thinned Linear Arrays in Interferometric Aperture Synthesis Radiometers

This letter deals with linear array design for satisfactory performance of system reliability in interferometric aperture synthesis radiometer (IASR), which is important to the instrument robustness against failures. We address the reliability performance in the case of one-element wreckage, as well as two other systematic metrics of IASR, i.e., spatial resolution and radiometric sensitivity. First, an array design model concerning the above three metrics is established within the framework of multiobjective optimization. Then, a state-of-the-art multiobjective method based on nondominated neighbor immune algorithm is applied. Finally, simulation results are presented to verify the effectiveness of designed array configurations.

Expansion Design of Interferometric Aperture Synthesis Arrays Based on Multi-Objective Optimization

This paper presents a study on expansion design of thinned arrays in interferometric aperture synthesis radiometers. The design problem deals with how to properly employ a limited number of antenna elements around a fixed array to fulfill performance requirements of interest, such as angular resolution, peak sidelobe level, and array redundancy. These factors of merit are grouped and discussed within the framework of multi-objective design. Three state-of-the-art optimizers based on multi-objective particle swarm optimization, non-dominated sorting genetic algorithm II, and non-dominated neighbor immune algorithm are implemented for specific design examples. A comparative analysis is performed to point out the features and performances of the above-mentioned search methods.

Radiometric Sensitivity and Angular Resolution Optimization of Thinned Linear Arrays in Microwave Interferometric Radiometry

This communication addresses the thinned linear array design for the optimization of radiometric sensitivity and angular resolution, both of which are crucial characteristics of interferometric aperture synthesis radiometers (IASRs). First, we establish a multiobjective optimization model concerning their tradeoff. Then, three state-of-the-art search algorithms, namely, multiobjective particle swarm optimization (MOPSO), nondominated sorting genetic algorithm II (NSGA-II), and nondominated neighbor immune algorithm (NNIA), are implemented for the design of linear interferometric arrays. Furthermore, a constraint principle of interferometric arrays is proposed to reveal the inherent relation among array parameters closely related to IASR performances, which would be useful for the multiobjective optimization of IASR arrays.

Analysis and optimization of process energy consumption and environmental impact in electrical discharge machining of titanium superalloys

Abstract Process energy consumption and environmental impact have been considered as important performance indicators for the sustainable electrical discharge machining (EDM) process. This paper presents a sustainable manufacturing technique known as magnetic field-assisted EDM (MF-EDM) to enhance the machine characteristics for the purpose of reducing the energy consumption and environmental hazards of the conventional EDM machining Ti6Al4V. Firstly, the principles of energy consumption, machining noise impact, and MF-EDM are respectively described. Thereafter, a set of experiments is carried out to investigate the effects of the main process parameters on the electrode wear rate ( E W R ), energy consumption ( S E C ), and environmental impacts (including carbon emission and machining noise) extensively, using the Taguchi method. The results indicate that the pulse on time (ranging from 100 to 200 μs) and magnetic field intensity (ranging from 0.05 to 0.10 T) are the two most significant factors affecting the MF-EDM sustainable manufacturing performance. Furthermore, the optimal process parameters were obtained by optimizing the MF-EDM process economically and environmentally using the modified non-dominated neighbor immune algorithm (M-NNIA) method. Compared to the minimum outputs of the experimental results, the optimal solutions of the E W R , S E C , and machining noise were significantly decreased, by 18.30%, 61.43%, and 20.95%, respectively. Therefore, it can be concluded from the above research that the proposed hybrid MF-EDM technique offers significant advantages and potential for applications in the sustainable manufacturing field.

Magnetic-assisted method and multi-objective optimization for improving the machining characteristics of WEDM in trim cutting magnetic material

In wire electrical discharge machining (WEDM), increasing the machining characteristics is always a research hot spot. Now, magnetic-assisted method cannot directly improve the performance of WEDM during machining magnetic material. In addition, due to the defects of convergence precision, convergence speed, global optimization, and subjectivity, the application of individual parameter optimization algorithm is limited. In order to address these problems, this paper proposes magnetic-assisted method and modified non-dominated neighbor immune algorithm to ameliorate the performance of WEDM in trim cutting magnetic material. Firstly, the force analysis of debris and the analysis of discharge current waveform are carried out to clarify what setting condition of the magnetic field generator is proper. It is indicated that the magnetic-assisted method maybe improves the machining characteristics of WEDM when the Lorentz force has the same direction with dielectric flashing force during trim cutting magnetic material. Additionally, a set of trim cutting Taguchi experiment is accomplished to investigate the effect of magnetic-assisted method on material remove rate (MRR), surface roughness (SR), and recast layer thickness (RLT). Thereinto, it can be found that the machining characteristics of WEDM are obviously improved by magnetic-assisted method. More specifically, under magnetic-assisted method, MRR, SR, and RLT are improved by an average of 18.45, 17.86, and 17%, respectively. Then, on the basis of game theory, the modified non-dominated neighbor immune algorithm (NNIA) is proposed to select best process parameters for further increasing the machining characteristics of magnetic-assisted WEDM (MA-WEDM). Eventually, the comparative results of three algorithms and verification experiment demonstrate that this modified NNIA exhibits better optimization performance than NSGA-II and NNIA. Specifically, by modified NNIA, the largest increase of MRR and the greatest decrease of SR and RLT are 22.37, 44.59, and 15.07%, respectively.

Optimization of cascading failure on complex network based on NNIA

Recently, the robustness of networks under cascading failure has attracted extensive attention. Different from previous studies, we concentrate on how to improve the robustness of the networks from the perspective of intelligent optimization. We establish two multi-objective optimization models that comprehensively consider the operational cost of the edges in the networks and the robustness of the networks. The NNIA (Non-dominated Neighbor Immune Algorithm) is applied to solve the optimization models. We finished simulations of the Barabási–Albert (BA) network and Erdös–Rényi (ER) network. In the solutions, we find the edges that can facilitate the propagation of cascading failure and the edges that can suppress the propagation of cascading failure. From the conclusions, we take optimal protection measures to weaken the damage caused by cascading failures. We also consider actual situations of operational cost feasibility of the edges. People can make a more practical choice based on the operational cost. Our work will be helpful in the design of highly robust networks or improvement of the robustness of networks in the future.

Rolling scheduling of electric power system with wind power based on improved NNIA algorithm

This paper puts forth a rolling modification strategy for day-ahead scheduling of electric power system with wind power, which takes the operation cost increment of unit and curtailed wind power of power grid as double modification functions. Additionally, an improved Nondominated Neighbor Immune Algorithm (NNIA) is proposed for solution. The proposed rolling scheduling model has further improved the operation cost of system in the intra-day generation process, enhanced the system’s accommodation capacity of wind power, and modified the key transmission section power flow in a rolling manner to satisfy the security constraint of power grid. The improved NNIA algorithm has defined an antibody preference relation model based on equal incremental rate, regulation deviation constraints and maximum & minimum technical outputs of units. The model can noticeably guide the direction of antibody evolution, and significantly speed up the process of algorithm convergence to final solution, and enhance the local search capability.

Constrained nondominated neighbor immune multiobjective optimization algorithm for multimedia delivery

In recent years, artificial immune system (AIS) algorithms is considered to be an effective method to solve the multiobjective optimization problems (MOPs), such as multimedia delivery problem. Though a decent number of solution algorithms have been proposed for MOPs, far less progress has been made for constrained multiobjective optimization problems (CMOPs), which demands a combination of constraints handling technique and search algorithm, e.g. Nondominated Neighbor Immune Algorithm (NNIA). In this paper, we propose a hybrid constraint handling technique of adaptive penalty function and objectivization of constraint violations. In our approach, the dominant population is updated via a method of objectivization of constraint violations and proportional reduction while a modified adaptive penalty function method based on the structure of the search algorithm (NNIA) is utilized to update the active population. We combine the proposed hybrid constraint handling method with NNIA to form the proposed Constrained Nondominated Neighbor Immune Algorithm (C-NNIA) to address the constrained multiobjective optimization problems. To our knowledge, it is the first time NNIA has been applied as the search algorithm for CMOPs. Numerical simulations indicate that the proposed algorithm outperforms the current state-of-the-art algorithms, i.e. NSGA-II-WTY, in both convergence and diversity.

Change detection in synthetic aperture radar images based on evolutionary multiobjective optimization with ensemble learning

This paper presents an unsupervised change detection approach for synthetic aperture radar (SAR) images based on a multiobjective clustering algorithm and selective ensemble strategy. A multiobjective clustering method based on the nondominated neighbor immune algorithm is proposed for classifying changed and unchanged regions in the difference image, which aims at reducing the effect of speckle noise and enhancing the cluster performance. The proposed multiobjective clustering method generates a set of mutually intermediate clustering solutions, which correspond to different trade-offs between the two objectives: restraining noise and preserving detail. Then the selective ensemble strategy is introduced to integrated theses intermediate change detection results. Experiments on real SAR images show that the proposed change detection method based on multiobjective clustering reduces the effect of speckle noise and enhancing the cluster performance. In general, the proposed method makes a balance between noise-immunity and the preservation of image detail. The final change detection results obtained by the selective ensemble strategy exhibit lower errors than other existing methods.

Multi-objective immune algorithm with Baldwinian learning

By replacing the selection component, a well researched evolutionary algorithm for scalar optimization problems (SOPs) can be directly used to solve multi-objective optimization problems (MOPs). Therefore, in most of existing multi-objective evolutionary algorithms (MOEAs), selection and diversity maintenance have attracted a lot of research effort. However, conventional reproduction operators designed for SOPs might not be suitable for MOPs due to the different optima structures between them. At present, few works have been done to improve the searching efficiency of MOEAs according to the characteristic of MOPs. Based on the regularity of continues MOPs, a Baldwinian learning strategy is designed for improving the nondominated neighbor immune algorithm and a multi-objective immune algorithm with Baldwinian learning (MIAB) is proposed in this study. The Baldwinian learning strategy extracts the evolving environment of current population by building a probability distribution model and generates a predictive improving direction by combining the environment information and the evolving history of the parent individual. Experimental results based on ten representative benchmark problems indicate that, MIAB outperforms the original immune algorithm, it performs better or similarly the other two outstanding approached NSGAII and MOEA/D in solution quality on most of the eight testing MOPs. The efficiency of the proposed Baldwinian learning strategy has also been experimentally investigated in this work.

Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm

Community structure is one of the most important properties in social networks, and community detection has received an enormous amount of attention in recent years. In dynamic networks, the communities may evolve over time so that pose more challenging tasks than in static ones. Community detection in dynamic networks is a problem which can naturally be formulated with two contradictory objectives and consequently be solved by multiobjective optimization algorithms. In this paper, a novel multiobjective immune algorithm is proposed to solve the community detection problem in dynamic networks. It employs the framework of nondominated neighbor immune algorithm to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. The problem-specific knowledge is incorporated in genetic operators and local search to improve the effectiveness and efficiency of our method. Experimental studies based on four synthetic datasets and two real-world social networks demonstrate that our algorithm can not only find community structure and capture community evolution more accurately but also be more steadily than the state-of-the-art algorithms.

Guest editorial: Memetic Algorithms for Evolutionary Multi-Objective Optimization

This thematic issue on Memetic Algorithms for Evolutionary Multi-Objective Optimization was initially motivated by the success of Memetic Algorithms (MAs) in a variety of real-world problems. Among them, the multi-objective class of problems is of great interest. This issue brings together some of the latest research developments in this field, with two papers highlighting some of the fundamental algorithmic issues and another paper focusing on a specific real-world application. The paper by Gong et al. proposed a memetic algorithm to address static multi-objective optimization problems. The proposed algorithm, namely Multi-objective Lamarckian ImmuneAlgorithm (MLIA), employs the general framework of Non-dominated Neighbor Immune Algorithm. Lamarckian learning was embedded in this framework as the local search method. Empirical studies showed that, with the incorporation of Lamarckian learning, MLIA achieved very appealing performance in comparison with some state-ofthe-art multi-objective evolutionary algorithms, such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D). Dynamic optimization has attracted significant research interests in recent years. In their work, Wang and Li

Hybrid immune algorithm with Lamarckian local search for multi-objective optimization

Lamarckian learning has been introduced into evolutionary computation as local search mechanism. The relevant research topic, memetic computation, has received significant amount of interests. In this study, a novel Lamarckian learning strategy is designed for improving the Nondominated Neighbor Immune Algorithm, a novel hybrid multi-objective optimization algorithm, Multi-objective Lamarckian Immune Algorithm (MLIA), is proposed. The Lamarckian learning performs a greedy search which proceeds towards the goal along the direction obtained by Tchebycheff approach and generates the improved progenies or improved decision vectors, so single individual will be optimized locally and the newcomers yield an enhanced exploitation around the nondominated individuals in less-crowded regions of the current trade-off front. Simulation results based on twelve benchmark problems show that MLIA outperforms the original immune algorithm and NSGA-II in approximating Pareto-optimal front in most of the test problems. When compared with the state of the art algorithm MOEA/D, MLIA shows better performance in terms of the coverage of two sets metric, although it is laggard in the hypervolume metric.