Immune Optimization Algorithm Research Articles

A Kernel-Based Intuitionistic Fuzzy C-Means Clustering Using Improved Multi-Objective Immune Algorithm

Clustering algorithms have attracted a lot of attentions recently in real-world applications. However, the traditional clustering algorithms still have plenty of defects which are not yet resolved. In this paper, a kernel-based intuitionistic fuzzy C-means clustering using improved multi-objective artificial immune algorithm (KIFCM-IMOIA) is proposed. In our algorithm, the kernel trick and the intuitionistic fuzzy entropy (IFE) are introduced into the objective functions, which improves the robustness to noises. In addition, an improved multi-objective optimization immune algorithm (IMOIA), which simultaneously optimizes the intra-cluster compactness and inter-cluster separation, is proposed to prevent the algorithm from falling into local optimum. The proposed IMOIA uses a novel active antibody selection strategy, a hybrid differential evolution strategy, and an adaptive mutation operator to maintain better distribution of the solutions with better convergence. Finally, we performed experiments using 14 UCI datasets and compared our algorithm with six clustering methods on three performance metrics. The experimental results show that our algorithm performs better than other algorithms.

Geometrical structures of trimetallic Ag–Pd–Pt and Au–Pd–Pt clusters up to 147 atoms

The core-shell morphologies of (PdPt)coreAgshell and (PdPt)coreAushell up to 147 atoms are investigated. The structural optimization of M–Pd–Pt (M = Ag or Au) is carried out by using adaptive immune optimization algorithm with constructed inner cores (AIOA-IC), which is described by the many-body Gupta potential. Results show that in Ag-rich AgnPd12Pt12 (n = 1–60) clusters, amorphous motifs with icosahedral fragments are the dominant motifs. However, in Au-rich AunPd12Pt12 clusters, icosahedra are the dominant motifs, which are competitive with decahedra in this size range. Furthermore, for the 100- and 147-atom M–Pd–Pt clusters, there exists significant structural difference. On the other hand, the atomic distribution of M–Pd–Pt clusters is analyzed by the order parameter and the number of bonds. Results show that Pd atoms in AgnPd12Pt12 clusters have more mixed pattern with Ag and Pt atoms than in AunPd12Pt12 clusters. It is further explained by surface energy, atomic radius, and cohesive energy.

Structural characteristics of 98-atom Co-Pt clusters

The well-mixed morphology of 98-atom Co-Pt clusters is investigated. The global minimal structures of ComPt98-m (m = 1–97) clusters are optimized by adaptive immune optimization algorithm with inner cores (AIOA-IC) method based on the many-body Gupta potential. In Co-poor regions, the clusters are found to adopt face-centered cubic (fcc) and stacking-fault fcc (sf-fcc) motifs while in Co-rich regions, they present icosahedral motifs. Leary tetrahedron (LT) appears to be the dominant motif among all compositions. A Td point group is found in Co80Pt18 cluster, and the construction method for the Td LT is proposed starting from a regular tetrahedron. Moreover, the Co-Pt clusters favor well-mixed pattern except for Co-rich and Co-poor regions and the highest degree of mixing is at Co55Pt43, which is one of the 14 amorphous motifs but not LT motif. The phenomenon of atomic rearrangement is also reported.

A Hybrid Tabu Search-Based Artificial Immune Algorithm for Construction Site Layout Optimization

Layout of temporary facilities on a construction site is essential to enhance productivity and safety. It is a complex issue due to the unique nature of construction. This problem is validated as an NP-hard and one of the challenging problems in the field of construction management. In this paper, we proposed a hybrid algorithm, named topt-aiNet, to solve the construction site layout problem by combining the aiNet algorithm with Tabu search. Experimental results showed that the proposed algorithm outperformed the stateof-the-art ones. DOI: 10.32913/rd-ict.vol2.no15.470

Robust Image Segmentation Method for Cotton Leaf Under Natural Conditions Based on Immune Algorithm and PCNN Algorithm

In the actual cotton planting environment, rapid change of light within a day, reflection from different backgrounds and different weather conditions can affect the imaging of cotton. Therefore, the crop object segmentation is difficult. Images which were captured in 12 natural scenes during cotton planting, including three weather conditions, such as sunny, cloudy and rainy and four soil cover conditions, such as white mulch film, black mulch film, straw and bare soil were regarded as the research objects. This paper presents the cotton leaf segmentation method based on Immune algorithm and pulse coupled neural networks (PCNN). First, 17 color components of white mulch film, black mulch film, straw, bare soil and cotton under the conditions of sunny, cloudy and rainy days were analyzed by using statistical method. Three high feasible and anti-light color components were selected by histogram statistical with mean gray value. Second, the optimal parameters of PCNN model and the optimal number of iterations were determined by using immune algorithm optimization theory, and the method in this paper was tested by using 1200 cotton images which were captured under 12 natural scenes. Finally, the test results showed that this method can distinguish cotton target region from soil and other background regions. Meanwhile, for reflection of mulch film, crop shadow, dark light, complex background, noise, etc. which are often appeared in natural scene, four image segmentation methods of Otsu algorithm, [Formula: see text]-Means algorithm, FCM algorithm and PCNN were compared with the proposed method in this paper. The segmentation result showed that the proposed method has good resistance to change of light and complex background. The average [Formula: see text] of the proposed method is 6.5%, significantly lower than that of other four methods and the performance is better than other four methods. This method can segment cotton images in different weather conditions and different backgrounds accurately under complex natural conditions. It will contribute to the subsequent growth status determination and pest diagnosis of cotton.

A Hybrid Tabu Search-Based Artificial Immune Algorithm For Construction Site Layout Optimization

A Hybrid Tabu Search-Based Artificial Immune Algorithm For Construction Site Layout Optimization

ARBIC: An Adjustable Range Based Immune hierarchy Clustering protocol supporting mobility of Wireless Sensor Networks

Introducing the mobility to Wireless Sensor Networks (WSNs) puts new challenges in designing an energy-efficient routing. Improving the network lifetime and the packet delivered rate are the most important issues in designing of the Mobile Wireless Sensor Networks (MWSNs). MWSN is more difficult to deal with than its stationary counterpart because it does not have a fixed topology. This increases the complexity of routing due to the frequent link breaks between clusters and their members. Various clustering protocols are developed to support mobility of the nodes in the WSNs. However, these protocols suffer from some limitations in connectivity, energy-efficient, fault tolerance, load balancing and mobility adaption because they organize the network into fixed size clusters and select the heads of these clusters randomly. Thus, this paper proposes an Adjustable Range-Based Immune hierarchy Clustering protocol (ARBIC) with mobility supporting to deliver the sensory data of the MWSN to the base station in an efficient way for a long-time. The operation of ARBIC protocol depends on organizing the network into optimum clusters and adjusting the size of these clusters based on the speed of the mobile sensor nodes to preserve the cluster connectivity. ARBIC protocol utilizes the immune optimization algorithm to determine the best positions of the clusters’ heads that optimize the trade-off among the mobility factor, energy consumption, connectivity, residual energy and link connection time. In order to save the overhead packets and the computational time, the ARBIC protocol runs the clustering process if and only if the residual energy of any cluster head is less than a predefined energy threshold. Moreover, it performs a fault tolerance mechanism after sending each frame to reduce the packets drop rate by maintaining the stability of links between the clusters’ heads and their member nodes. Mathematical analyses are established to analyze the computational and overhead complexities of the ARBIC protocol. Simulation results show that, compared with other protocols, the ARBIC protocol can effectively improve the packet delivery ratio while simultaneously offering lower energy consumption and delay by using sensor nodes with adjustable transmission ranges.

Quantum-inspired immune clonal algorithm for railway empty cars optimization based on revenue management and time efficiency

By proposing the concept of timeline, transform dynamic vehicle scheduling problem into a series of static vehicle scheduling problems. With the objective function of benefit maximization, the cloud preference model of dynamic empty car scheduling is built considering empty car delay time constraint. The non-dominated antibodies are proportionally immune clonal according to their cloud preference, which are defined by their cloud application preferences. It is beneficial to enhance the forecasting accuracy of the immune gene manipulation, and to increase the speed of finding the optimal solution based on the application preference. Experimental results conclusively demonstrate the efficiency and effectiveness of the improving system availability, load balancing deviation and valid time brought by the proposed algorithm in cloud computing environments, conditions and that more close to the reality, empty car scheduling model for specific time was established.

Large scale structural optimization of trimetallic Cu–Au–Pt clusters up to 147 atoms

The stable structures of Cu–Au–Pt clusters up to 147 atoms are optimized by using an improved adaptive immune optimization algorithm (AIOA-IC method), in which several motifs, such as decahedron, icosahedron, face centered cubic, sixfold pancake, and Leary tetrahedron, are randomly selected as the inner cores of the starting structures. The structures of Cu8AunPt30-n (n=1–29), Cu8AunPt47-n (n=1–46), and partial 75-, 79-, 100-, and 147-atom clusters are analyzed. Cu12Au93Pt42 cluster has onion-like Mackay icosahedral motif. The segregation phenomena of Cu, Au and Pt in clusters are explained by the atomic radius, surface energy, and cohesive energy.

A novel dynamic reactive power planning methodology to enhance transient voltage stability

Summary Transient voltage stability (TVS) is an important issue for security operation with the development of power system topology and loads. Lots of research on reactive power planning mainly focus on static voltage stability. But the research on the dynamic reactive power planning considering TVS is very lacking so far. And most of them are only analyzed on some serious buses without covering all the weak faults. And some research considering TVS are limited under some conditions and not general enough for large-scale applications. Therefore, it is an urgent task to propose a feasible dynamic reactive power planning methodology to enhance TVS. So a novel dynamic reactive power planning methodology is proposed in this paper. In addition, although some TVS assessment indices have been proposed, they may not be suitable to be used in dynamic reactive power planning in the iteration calculation. To evaluate voltage stability after contingencies, a TVS assessment index is presented. Then an approach based on compensation sensitivity analysis is discussed for the best dynamic volt ampere reactive planning candidate locations. And a dynamic reactive power planning optimization methodology based on the memetic immune algorithm for multiobjective optimization is proposed to enhance TVS with the least static volt ampere reactive compensator investment cost and power loss cost. The methodology can cover all faults through random faults selection in the optimization. Afterwards, IEEE 39 power system and Zhejiang power grid in China are illustrated and analyzed for the improvement of TVS. It is proved that the proposed dynamic reactive power planning methodology is effective and beneficial to the security operation for power system.

Based on Immune Optimization Algorithm and Hybrid Particle Swarm Optimization Algorithm Research on Customized Bus Station Setting and Route Planning—A Case Study of Customized Public Transport in Wuhan, China

Customized bus is a kind of new type of public transport service which is tailor-made for the people whose travel has a similar location, time, destination and so on. In 2013, it has appeared in China and landed in various cities successively. But the development around the situation is different, the study about this problem is few. Based on the immune optimization algorithm and particle swarm optimization algorithm, combined with the advantages of genetic algorithm, the paper forms a hybrid immune optimization algorithm and hybrid particle swarm algorithm. Considering the custom bus station settings, line planning and other aspects, we take custom bus in Wuhan City, Hubei Province as an example, and try to give a scientific and practical guidance to the development of customized public transport nowadays, so that it can get a further theoretical and practical basis on mathematics in its development and operation.

Influence of noble metal dopants (M = Ag, Au, Pd or Pt) on the stable structures of bimetallic Co-M clusters

The core-shell and well-mixed morphologies of Co-M (M = Ag, Au, Pd or Pt) clusters are investigated. The interatomic interaction of these clusters is described by the many-body Gupta potential, and the stable structures are optimized using adaptive immune optimization algorithm. Results show that all 55-atom Co-Au and Co-Pd clusters adopt Mackay icosahedral motifs, but Co-Ag and Co-Pt clusters have more configurations. The number of bonds indicates that Co-Ag, Co-Au and Co-Pd clusters take the core-shell structures. In these core-shell structures, Co atoms occupy the inner core, and Ag and Pd atoms tend to scatter on the surface, but Au atoms prefer packing together, which is explained by atomic radii, cohesive energy and surface energy. However, the investigated Co-Pt clusters favor well-mixed pattern. Furthermore, there exists difference among the relative stability of these clusters by energy analysis. Moreover, the influence of the noble metals on the structures of Co13Mn (n = 1–41) clusters are studied. The atomic distribution of Co and noble metals in Co13Mn clusters is analyzed, which is consistent with that in 55-atom Co-M clusters.

基于免疫算法的碳纳米管线传感器网络优化配置

针对三维编织复合材料结构健康监测中碳纳米管线传感器网络的优化配置问题，根据检测覆盖范围最大且传感器数量最小的原则，提出了一种基于免疫算法的求解方案。该方案将要求解的传感器位置作为抗体，设计了抗体编码方式以及免疫算法的过程。仿真实验结果表明，免疫算法能够有效地求得最佳解决方案。 According to the optimization allocation of three-dimensional braided composite material of carbon nano pipeline sensor in the sensors network to monitor material structure and keep structure health, in consideration of the principle of coverage maximum and sensor number minimum, an immune optimization algorithm was proposed. It designed individual antibody coding and the process of the immune algorithm. Finally, simulation results show that the immune algorithm can effectively obtain the best solution.

Geometrical optimization of Cu-Au-Pd clusters based on the construction of inner cores

The trimetallic cluster has become a hot topic in the field of basic scientific research due to its special catalytic, magnetic and chemical activities. It is very important to determine the stable structures of clusters. In order to optimize the stable structure of large size Cu-Au-Pd cluster, a modification algorithm of adaptive immune optimization algorithm based on the construction of inner cores, called AIOA-IC algorithm, is proposed. The only difference between AIOA and AIOA-IC lies in their starting structures. Instead of generating the starting structure randomly in AIOA, an inner core in the AIOA-IC method is used for generating the starting structure. Several motifs, such as decahedron, icosahedron, face centered cubic, six-fold pancake structure, and Leary tetrahedron, are randomly selected as the inner cores. The size of the inner core is determined according to the cluster size. The Gupta potential based on the second moment approximation of tight binding potential is used to describe the interatomic interaction between Cu-Au-Pd clusters, and the corresponding potential parameters, such as the cohesive energy, lattice constants, and elastic constants are obtained by fitting the experimental values. To test the efficiency of the proposed algorithm, the stable structure of Ag-Pd-Pt cluster with 60 atoms is optimized. The results show that the new structure has lower energy than the cluster reported in the literature. It can be seen that the AIOA-IC algorithm has a stronger ability to search for the potential energy surface of the Gupta potential. Furthermore, the proposed algorithm is used to optimize the stable structures of 38-atom and 55-atom Cu-Au-Pd clusters. The structures of the investigated Cu6AunPd32-n, CunAu6Pd32-n and CunAu32-nPd6 (n=1-31) clusters can be categorized into three types:five-fold, six-fold, and truncated octahedron. Moreover, it is found that the compositions of Cu, Au and Pd atoms in the trimetallic clusters affect the structural type of the cluster. However, the Cu13AunPd42-n, CunAu13Pd42-n, and CunAu42-nPd13 (n=1-41) clusters each have a structure of complete Mackay icosahedron. Furthermore, the order parameter results show that Cu, Au and Pd atoms each have a significant segregation phenomenon. For the 147-atom Cu12Au93Pd42 cluster, the structure is also of an icosahedron. The central atom is Au, and the inner shell and sub-outer shell are occupied by 12 Cu and 42 Pd atoms, respectively. The outer shell is filled with 92 Au atoms. The results show that the Cu, Pd and Au atoms tend to be distributed in the inner shell, sub-outer shell, and outer shell, respectively. This can be further explained by the results of the atomic radius and the surface energy.

An Adaptive Multi-objective Immune Algorithm for Optimal Design of Truss Structures

In this paper, an adaptive immune clone selection algorithm for multi-objective optimization (AICSAMO) is proposed. A novel adaptive polynomial mutation operator with dynamic mutation probability is employed in AICSAMO. This adaptive mutation operator executes a rapid global search at the earlier stage of the algorithm and a fine-tuning search at the later stage of the algorithm, which adopts generation-dependent parameters to improve the convergence speed and global optimum searching ability. The effectiveness of AICSAMO is evaluated through the truss sizing and shape optimization problems of a 10-bar plane truss and a 25-bar space truss. According to the comparison of AICSAMO with various multi-objective optimization algorithms developed recently, the simulation results illustrate that AICSAMO has remarkable performance in finding a wider spread of optimal solutions and in maintaining better uniformity of the solutions with better convergence.

A novel multi-population coevolution strategy for single objective immune optimization algorithm

A novel multi-population coevolution strategy for single objective immune optimization algorithm (MCIA) is proposed to solve numerical and engineering optimization problem in real world from the inspiration that how neuro-endocrine system affects T cells and B cells in immune system eliminate the danger. The main idea of MCIA is to promote three populations to coevolution through self-adjusted clone operator, the applied dislocation arithmetic crossover, cloud self-adapting mutation operator and local search operator to produce lymphocyte with high affinity, where several operators have the capability of broadening the elites search space, boosting the global and local search around elites in search space. The MCIA is population B, population T, and assistant population A carrying on parallel evolution in nature, which simulates the immune system more comprehensively and unique in the aspects: clone operator and selected elite elements in the memory population enable the search space be broadened and compressed, and with the help of the cloud model characterized with randomness and stable topotaxis and local search technique, the global and local search is integrated to find the global optima with high population diversity. The performance comparisons of MCIA with three known immune algorithms and three optimization algorithms in optimizing 12 benchmark functions indicate that MCIA is an effective algorithm for solving global optimization problems with high precision, good robustness and low time complexity.

Adaptive immune genetic algorithm for weapon system portfolio optimization in military big data environment

Military applications are producing massive amounts of data due to the use of multiple types of sensors on the battlefield. The aim of this paper is to investigate the weapon system portfolio probl...

Structure and bonding in quaternary Ag[sbnd]Au[sbnd]Pd[sbnd]Pt clusters

Structural optimization of quaternary AgAuPdPt clusters up to 38 atoms is performed using adaptive immune optimization algorithm, and the Gupta many-body potential is adopted to model interatomic interaction. By fixing the atomic number of three types and varying the number of one type in quaternary clusters, geometrical effects by different types of noble metals are investigated. Results show that most configurations present the growth pattern based on the 19-atom double icosahedra, however, the structural transition model induced by increasing the number of the fourth noble atom is found to be different. Furthermore, the order parameter indicates that in quaternary clusters Ag and Au atoms mainly occupy the surface sites, Pd atoms tend to be in the middle shell, and the inner shell is located by Pt atoms. The study of the number of bonds indicates that Ag and Au atoms prefer scattering and forming a circle on the surface, respectively.

Modeling and identification of the multi-axis drive system using support vector machine and granular computing

Advanced manufacturing technology requires high-precision capability in multi-axis computer numerical control (CNC) machine tools. At present, the modeling and identification for the drive system of CNC machine tools has some defects. In order to solve the problem, some interdisciplinary theories and methods, such as support vector machines, granular computing, artificial immune algorithms, and particle swarm optimization algorithms, have been used to model and identify multi-axis drive systems for CNC machine tools. An identification method using a support vector machine, based on granular computing, is presented to identify a multi-axis servo drive system model for improving the precision of model identification, and an immune particle swarm optimization algorithm, based on crossover and mutation functions, is proposed to optimize the structure parameters of the support vector machine based on granular computing. The proposed identification method was evaluated by experiments using the multi-axis servo drive system. The experimental results showed that the proposed approach is capable of improving modeling and identification precision.

Big wave of the intelligent connected vehicles

In order to meet the exponentially increasing demand on mobile data traffic, self-backhaul ultra-dense networks (UDNs) combined with millimeter wave (mmWave) communications are expected to provide high spatial multiplexing gain and wide bandwidths for multi-gigabit peak data rates. In self-backhaul UDNs, how to make the radio access rates of small cells match their backhaul rates by user association and how to dynamically allocate bandwidth for the access links and backhaul links to balance two-hop link resources are two key problems on improving the overall throughputs. Based on this, a joint scheme of user association and resource allocation is proposed in self-backhaul ultra-dense networks. Because of the combinatorial and nonconvex features of the original optimization problem, it has been divided into two subproblems. Firstly, to make the radio access rates of small base stations match their backhaul rates and maximize sum access rates per Hz of all small cells, a proportional constraint is introduced, and immune optimization algorithm (IOA) is adopted to optimize the association indicator variables and the boresight angles of between users and base stations. Then, the optimal backhaul and access bandwidths are calculated by differentiating the general expression of overall throughput. Simulation results indicate that the proposed scheme increases the overall throughputs significantly compared to the traditional minimum-distance based association scheme.