Immune Genetic Algorithm Research Articles

Optimal Allocation of ESSs for Mitigating Fluctuation in Active Distribution Network

As distributed generations (DGs) are occupying an increasing proportion in active distribution network, the fluctuation of bus voltage becomes severe because of their intermittent and stochastic characteristics. Energy Storage System (ESS) can be adopted as an effective means to mitigate this fluctuation. This paper proposes a methodology for the optimal allocation of ESSs based on the novel optimal affine power flow (OAPF) approach. Affine models of wind turbine generation (WTG), photovoltaic (PV) system, and ESS are built while considering uncertainty characters of their power outputs. The objective function is set to minimize the capital investment of ESSs and the penalty costs of bus voltage fluctuations. The complex affine arithmetic based distribution power flow is used to ensure that constraints on power flow limits, voltage limits, and ESS operational limits are satisfied. The optimization problem is solved by an improved immune genetic algorithm (IIGA). The proposed approach is verified via a modified IEEE 33-bus system with a high penetration of DGs. Results show that the optimal allocation of ESSs can satisfy both the technical and economic requirements under such an uncertain environment.

Multi-objective immune genetic algorithm solving nonlinear interval-valued programming

This work studies one multi-objective immune genetic algorithm with small population to solve a general kind of unconstrained multi-objective interval-valued programming. In this optimization approach, those competitive individuals are discriminated based on interval arithmetic rules and a possibility model; a crowding degree model in interval-valued environments is developed to eliminate redundant individuals; the current population promotes different individuals to evolve towards specific directions by population sorting and immune evolution, while those elitist individuals found accelerate to explore the desired regions through genetic evolution. The theoretical analysis has showed that the computational complexity of the proposed approach depends mainly on the elitist population size. Comparative experiments have illustrated that the approach can take a rational tradeoff between effect and efficiency. It can perform well over the compared approaches as a whole, and has the potential to solving multi-modal and hard multi-objective interval-valued programming problems.

A universal prototype design framework of the stem and stern contours of hull surface and the self-adaptive solving strategy

Ship geometry reconstruction technology is a key technique and research focus in the ship design. And the optimization algorithm used in the ship design is always a hot research area. To establish the control points’ distribution model database of characteristic curves, based on the NURBS control mesh of the hull surface, an approach to approximate the initial stem and stern contours of hull form modeling using the concept of parametric generation is proposed. The optimization model is constructed with the homogeneous coordinate component of the fitted contour’s control points as the design variables. The objective function is set to minimize the maximum relative difference among the longitudinal coordinates of the approximated contour and the original ones corresponding to the same drafts. The appropriate constraints are set according to the characteristics of the contours and the geometrical characteristic of NURBS. Considering a number of empirical knowledge existing in the process of ship design, and the conventional quantum-behaved particle swarm optimization (QPSO) algorithm depending upon good initial population, the immune quantum-behaved particle swarm optimization (IQPSO) is specifically designed and used to solve this complex nonlinear constrained optimization problem through the immune operator of the immune genetic algorithm combining with the QPSO algorithm. Meanwhile, the self-adaptive constraint evolution strategy is proposed to improve the IQPSO algorithm. The simulation results of the full-scale ship’s contours demonstrate the feasibility and effectiveness of the proposed algorithm and method.

The Evaluated Measurement of a Combined Genetic Algorithm and Artificial Immune System

This paper demonstrates a hybrid between two optimization methods which are the Artificial Immune System (AIS) and Genetic Algorithm (GA). The novel algorithm called the immune genetic algorithm (IGA), provides improvement to the results that enable GA and AIS to work separately which is the main objective of this hybrid. Negative selection which is one of the techniques in the AIS, was employed to determine the input variables (populations) of the system. In order to illustrate the effectiveness of the IGA, the comparison with a steady-state GA, AIS, and PSO were also investigated. The testing of the performance was conducted by mathematical testing, problems were divided into single and multiple objectives. The five single objectives were then used to test the modified algorithm, the results showed that IGA performed better than all of the other methods. The DTLZ multiobjective testing functions were then used. The result also illustrated that the modified approach still had the best performance.

A Hybrid Approach to Maximize the Lifetime of Directional Sensor Networks with Smoothly Varying Sensing Ranges

The difficulty of maximizing the lifetime in directional sensor networks has gained increasing attention recently. Most of the existing studies are focused on directional sensors with single or several predefined sensing ranges. In the present study, directional sensors can change sensing ranges smoothly. We address the problem of maximizing the lifetime in directional sensor networks with such smoothly varying sensing ranges,and propose a hybrid approach that combines a column generation method with an immune genetic algorithm. We search for attractive columns with the genetic algorithm, and optimize them by designing dynamic vaccines. Computational results demonstrate the performance of the proposed approach. Meanwhile, the advantage of the mentioned sensors in terms of solution quality is also revealed.

Therapy Algorithm Based on Medical Operator

The gene pool operator, methods and ideas in Chinese medical therapy are introduced into the genetic algorithm, a therapy algorithm based on medical operator is proposed. Specific methods and steps to implement the algorithm is given, which applies in the optimal solution for the 0-1 problem. Contrasted with standard genetic algorithm and immune genetic algorithm (IGA) on the 0-1 problem, this algorithm is with fast convergence, effective and feasible, having characteristics to avoid premature and overcome effectively the degradation in Math-lab simulation tests.

Reducing Building Conflicts in Map Generalization with an Improved PSO Algorithm

In map generalization, road symbolization and map scale reduction may create spatial conflicts between roads and neighboring buildings. To resolve these conflicts, cartographers often displace the buildings. However, because such displacement sometimes produces secondary spatial conflicts, it is necessary to solve the spatial conflicts iteratively. In this paper, we apply the immune genetic algorithm (IGA) and improved particle swarm optimization (PSO) to building displacement to solve conflicts. The dual-inheritance framework from the cultural algorithm is adopted in the PSO algorithm to optimize the topologic structure of particles. We generate Pareto optimal displacement solutions using the niche Pareto competition mechanism. The results of experiments comparing IGA and the improved PSO show that the improved PSO outperforms IGA; the improved PSO results in fewer graphic conflicts and smaller movements that better satisfy the movement precision requirements.

A Model for the Stop Planning and Timetables of Customized Buses.

Customized buses (CBs) are a new mode of public transportation and an important part of diversified public transportation, providing advanced, attractive and user-led service. The operational activity of a CB is planned by aggregating space–time demand and similar passenger travel demands. Based on an analysis of domestic and international research and the current development of CBs in China and considering passenger travel data, this paper studies the problems associated with the operation of CBs, such as stop selection, line planning and timetables, and establishes a model for the stop planning and timetables of CBs. The improved immune genetic algorithm (IIGA) is used to solve the model with regard to the following: 1) multiple population design and transport operator design, 2) memory library design, 3) mutation probability design and crossover probability design, and 4) the fitness calculation of the gene segment. Finally, a real-world example in Beijing is calculated, and the model and solution results are verified and analyzed. The results illustrate that the IIGA solves the model and is superior to the basic genetic algorithm in terms of the number of passengers, travel time, average passenger travel time, average passenger arrival time ahead of schedule and total line revenue. This study covers the key issues involving operational systems of CBs, combines theoretical research and empirical analysis, and provides a theoretical foundation for the planning and operation of CBs.

Voltage and reactive power optimisation of offshore wind farms based on terminal voltage control mode of DFIG

In order to solve the problem that the voltage of the collector system rises and the loss increases in the offshore wind farm, the reactive power adjustment capability of the doubly-fed induction generator (DFIG) is studied. This study presents a method to optimise the system voltage and network loss with the terminal voltage of DFIG as the control target. The immune genetic algorithm is used to solve the voltage and reactive power optimisation problem of offshore wind farm in ensuring the voltage is within the allowable range when the active power out of DFIG changes. So that the reactive power adjustment capability of DFIG is fully used, both the system network loss and the reactive power compensation capacity are reduced, which verifies the correctness of the method.

Multi-Cell Cooperative Outage Compensation in Cloud-RANs Based 5G Public Safety Network

Cell outage compensation is a crucial way for public safety network (PSN) to recover network communication independently during disasters. As a significant technology for 5G, a cloud-radio access network (C-RAN) plays an important role in a PSN. C-RAN can better achieve multi-cell cooperative outage compensation under dense network coverage because of the characteristics of densification and centralization. Therefore, we propose an efficient multi-cell cooperative outage compensation convergence for the scene where more than one radio remote units (RRUs) are destructed in C-RAN-based PSN. This scheme compensates the network using both cooperative transmission and power adjustment. First, RRUs are selected to participate in the compensation according to the topology structure. Then, the problem model is built aiming at optimizing system outage probability. At the same time, the constraints of resources and service quality are considered. In addition, an enhanced immune-genetic algorithm is proposed innovatively to solve this NP-hard problem and to get the results of RRU transmit power parameters. Finally, simulation results demonstrate that the proposed algorithm convergences rapidly and our scheme reduces the PSN system outage probability effectively with the guarantee of users received power and the restriction of interference effects. The outage probability of the PSN system using the proposed method is 17.8% of the only cooperative transmit method and 3.9% lower than the only adjust power method.

A dynamic mathematical model of an ultra-supercritical coal fired once-through boiler-turbine unit

It is challenging and interesting to establish a precise dynamic model of an OTB (once-through boiler) power plant unit in order to meet large scale load demands from the power grid. This study proposes to establish such a dynamic mathematical model of an ultra-supercritical OTB unit under dry operating conditions. More precisely, the dynamic model structure was derived from mass and energy conservation laws as well as thermodynamic principles under some reasonable simplifications and assumptions. Then an IGA (immune genetic algorithm) was improved to identify the parameters, combined with running data. After this, to further enhance model performance, the dynamic mathematical model was extended to be the one with different sets of parameters and functions under different monotonous load ranges. Additionally, open- and closed-loop experiments were conducted in order to further validate the developed model. The experimental results show that the model outputs can approach the actual running data over a wide operating range with appropriate accuracy. More importantly, the dynamic model captures the essential dynamic characteristics of the unit. Therefore, the model can be feasible and applicable for simulation analysis and testing control algorithms.

UPQC voltage sag detection based on chaotic immune gentic algorithm

Photovoltaic generation is being rapidly developed as a clean energy source. The increased penetration of photovoltaics into power distribution networks has led to power-quality problems. A unified power-quality conditioner (UPQC) is an effective device for power-quality treatment and can compensate for voltage fluctuations and sags in a photovoltaic microgrid. This paper proposes a proportional---integral (PI) controller for detecting and compensating for voltage sags in a UPQC that is based on the chaotic immune genetic algorithm. Simulations showed that the proposed PI controller better compensates for the voltage than the traditional PI controller. Experimental results also showed that the new method can accurately detect and compensate for voltage sags in a photovoltaic microgrid in real time. The proposed PI controller can quickly detect voltage sags and improve the microgrid power quality; it provides a new approach to treating the power quality of a photovoltaic microgrid with a UPQC.

Uplink Scheduling of Navigation Constellation Based on Immune Genetic Algorithm.

The uplink of navigation data as satellite ephemeris is a complex satellite range scheduling problem. Large–scale optimal problems cannot be tackled using traditional heuristic methods, and the efficiency of standard genetic algorithm is unsatisfactory. We propose a multi-objective immune genetic algorithm (IGA) for uplink scheduling of navigation constellation. The method focuses on balance traffic and maximum task objects based on satellite-ground index encoding method, individual diversity evaluation and memory library. Numerical results show that the multi–hierarchical encoding method can improve the computation efficiency, the fuzzy deviation toleration method can speed up convergence, and the method can achieve the balance target with a negligible loss in task number (approximately 2.98%). The proposed algorithm is a general method and thus can be used in similar problems.

The Optimization Model and Algorithm of Remanufacturing Supply Chain Logistics Network with Option Contracts

By the point of view of remanufacturing enterprise, the studies of supply chain logistics network optimization problem focused on network optimization and logistics distribution between manufacturing enterprise and customer area. The present business environment is full of uncertainties. To compete effectively in such an environment, remanufacturing enterprises need to develop the capability of responding flexibly to changing market conditions and optimize effectively logistics networks. The mechanism of option contracts has effectively coordinated the relationship of remanufacturing enterprise and retailer and hedged against the risks of over- and under-production. There is few papers considers the mechanism of option contracts although there are lots of studies on supply chain logistics networks optimization problem. Therefore, the mechanism of option contracts is led into the study of remanufacturing supply chain logistics networks optimization problem. Considering environmental pollution influence factors in the remanufacturing process, a multi-objective optimization model of supply chain is developed. Dual-layer genetic algorithm mechanism was brought up. The first layer is responsible for supply chain logistics network structure with genetic algorithm. The second layer answers for specific supply chain logistics distribution with adaptive immune genetic algorithm. Finally, numerical examples demonstrate the validity of the model and algorithm for the optimization problem.

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...

A Self-adaptive Multipeak Artificial Immune Genetic Algorithm

Genetic algorithm is a global probability search algorithm developed by simulating the biological natural selection and genetic evolution mechanism and it has excellent global search ability, however, in practical applications, premature convergence occurs easily in the genetic algorithm. This paper proposes an self-adaptive multi-peak immune genetic algorithm (SMIGA) and this algorithm integrates immunity thought in the biology immune system into the evolutionary process of genetic algorithm, uses self-adaptive dynamic vaccination and provides a downtime criterion, the selection strategy of immune vaccine and the construction method of immune operators so as to promote the population develop towards the optimization trend and suppress the degeneracy phenomenon in the optimization by using the feature information in a selective and purposive manner. The simulation experiment shows that the method of this paper can better solve the optimization problem of multi-peak functions, realize global optimum search, overcome the prematurity problem of the antibody population and improve the effectiveness and robustness of optimization.

Economic dispatch of power system containing wind power and photovoltaic considering carbon trading and spare capacity variation

ABSTRACTThe drying up of the fossil energy sources and the damage from unchecked carbon emissions demand the development of low carbon economy, which promotes the development of new energy sources, such as wind power and photovoltaic. However, the direct connections of wind/photovoltaic power into power grid bring great impacts on power systems, thus affecting the security and stability of power system operations, which challenges the power system dispatching. In despite of many methods for power system dispatch, lack of the models, for power system containing wind power and photovoltaic considering carbon trading and spare capacity variation (PSCWPCCTSCV), restricts the further optimal operations of power systems. This paper studies the economic dispatch modeling problem of power system containing wind power and photovoltaic, establishes the model of economic dispatch of PSCWPCCTSCV. On this basis, adaptive immune genetic algorithm is applied to conduct the economic operation optimization, which can provide the optimal carbon trading price and the optimal power distribution coefficient. Finally, simulations based on the newly proposed models are made to illustrate the economic dispatch of PSCWPCCTSCV. The results show that optimization with the proposed model can not only weaken the volatility of the new energy effectively, but also reduce carbon emissions and reduce power generation costs.

A Novel Method for Mechanical Fault Diagnosis Based on Variational Mode Decomposition and Multikernel Support Vector Machine

A novel fault diagnosis method based on variational mode decomposition (VMD) and multikernel support vector machine (MKSVM) optimized by Immune Genetic Algorithm (IGA) is proposed to accurately and adaptively diagnose mechanical faults. First, mechanical fault vibration signals are decomposed into multiple Intrinsic Mode Functions (IMFs) by VMD. Then the features in time-frequency domain are extracted from IMFs to construct the feature sets of mixed domain. Next, Semisupervised Locally Linear Embedding (SS-LLE) is adopted for fusion and dimension reduction. The feature sets with reduced dimension are inputted to the IGA optimized MKSVM for failure mode identification. Theoretical analysis demonstrates that MKSVM can approximate any multivariable function. The global optimal parameter vector of MKSVM can be rapidly identified by IGA parameter optimization. The experiments of mechanical faults show that, compared to traditional fault diagnosis models, the proposed method significantly increases the diagnosis accuracy of mechanical faults and enhances the generalization of its application.

Expressway Maintenance Decision Model Based on Immune Genetic Algorithm

Expressway Maintenance Decision Model Based on Immune Genetic Algorithm

Trace Ratio Criterion-Based Kernel Discriminant Analysis for Fault Diagnosis of Rolling Element Bearings Using Binary Immune Genetic Algorithm

The rolling element bearing is a core component of many systems such as aircraft, train, steamboat, and machine tool, and their failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Due to misoperation, manufacturing deficiencies, or the lack of monitoring and maintenance, it is often found to be the most unreliable component within these systems. Therefore, effective and efficient fault diagnosis of rolling element bearings has an important role in ensuring the continued safe and reliable operation of their host systems. This study presents a trace ratio criterion-based kernel discriminant analysis (TR-KDA) for fault diagnosis of rolling element bearings. The binary immune genetic algorithm (BIGA) is employed to solve the trace ratio problem in TR-KDA. The numerical results obtained using extensive simulation indicate that the proposed TR-KDA using BIGA (called TR-KDA-BIGA) can effectively and efficiently classify different classes of rolling element bearing data, while also providing the capability of real-time visualization that is very useful for the practitioners to monitor the health status of rolling element bearings. Empirical comparisons show that the proposed TR-KDA-BIGA performs better than existing methods in classifying different classes of rolling element bearing data. The proposed TR-KDA-BIGA may be a promising tool for fault diagnosis of rolling element bearings.