A weight-based multiobjective immune algorithm: WBMOIA

The article combines Fruit Fly optimization algorithm with Immune Optimization Algorithm into Fruit Fly-Immune algorithm to solve the multi-distribution center location problem with a NP-hard nature. Using MATLAB simulation technology and comparing to the simulation results of traditional Immune Optimization Algorithm shows that the use of Fruit Fly-Immune algorithm to solve multi-distribution center location problem can get better convergence results and weaken faster.

In this paper, we present a weight-based multi-objective immune genetic algorithm (WBMOIGA). Compared to other weight-based multiobjective genetic algorithms, the proposed algorithm shows the following distinct characteristics: (1) a randomly weighted sum of multiple objectives is used as a fitness function, and (2) a local search procedure is utilized to improve the quality of the population, and an immune operator is adopted to increase the diversity of the population, and (3) specifically, a new mate selection operator, called tournament selection algorithm with similar individuals (TSASI), and a new environmental selection operator, named truncation algorithm with similar individuals (TASI), are presented. Simulation results on six standard test problems (ZDT1,ZDT2,ZDT3,ZDT6,SCH2,and FON) show WBMOIGA can find much better spread of solutions and better convergence near the true Pareto-optimal front compared to the elitist non-dominated sorting genetic algorithm (NSGA-II) and the random weight genetic algorithm (RWGA). Moreover, when applied to parallel machine scheduling, WBMOIGA also demonstrates better performance than NSGA-II and RWGA.

In this paper we propose a hybrid algorithm that can overcome the typical drawback of an artificial immune algorithm, namely, the propensity to runs slowly and experience a slower speed of convergence is than a genetic algorithm. Our hybrid algorithm combines the steepest descent algorithm with an artificial immune adaptive algorithm based on Euclidean distance. The hybrid algorithm fully displays global search ability and the global convergence of the immune algorithm. At the same time, the hybrid algorithm inserts a steepest descent operator to strengthen the local search ability. Experimental results show that the hybrid algorithm successfully improves the operational speed and convergence performance. In addition, this paper proves the convergence of the hybrid algorithm with a quasi-descent method.

With the rapid development of science and technology, ultrasound has been paid more and more attention by people, and it is widely used in engineering, diagnosis, and detection. In this paper, an ultrasonic image recognition method based on immune algorithm is proposed for ultrasonic images, and its method is applied to medical ultrasound liver image recognition. Firstly, this paper grays out the ultrasound liver image and selects the region of interest of the image. Secondly, it extracts the feature based on spatial gray matrix independent matrix, spatial frequency decomposition, and fractal features. Then, the immune algorithm is used to classify and identify the normal liver, liver cirrhosis, and liver cancer ultrasound images. Finally, based on the deficiency of the immune algorithm, it is combined with the support vector machine to form an optimized immune algorithm, which improves the performance of ultrasonic liver image classification and recognition. The simulation shows that this paper can effectively classify the normal liver, liver cirrhosis, and liver cancer ultrasound images. Compared with the traditional immune algorithm, this paper combines the immune algorithm with the support vector machine, and the optimized immune algorithm can effectively improve the performance of ultrasonic liver image classification and recognition.

A novel immune algorithm suitable for dynamic environments (AIDE) was proposed based on a biological immune response principle. The dynamic process of artificial immune response with operators such as immune cloning, multi-scale variation and gradient-based diversity was modeled. Because the immune cloning operator was derived from a stimulation and suppression effect between antibodies and antigens, a sigmoid model that can clearly describe clonal proliferation was proposed. In addition, with the introduction of multiple populations and multi-scale variation, the algorithm can well maintain the population diversity during the dynamic searching process. Unlike traditional artificial immune algorithms, which require randomly generated cells added to the current population to explore its fitness landscape, AIDE uses a gradient-based diversity operator to speed up the optimization in the dynamic environments. Several reported algorithms were compared with AIDE by using Moving Peaks Benchmarks. Preliminary experiments show that AIDE can maintain high population diversity during the search process, simultaneously can speed up the optimization. Thus, AIDE is useful for the optimization of dynamic environments.

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.

Intelligent control algorithms in dynamic multi-obstacle scenarios are prone to the problem of poor stability and reliability of their own motion. In order to solve the above core problems, firstly, the fuzzy set theory is used to adjust the three control parameters of PID control to realize the adaptive adjustment of the system when facing uncertain changes. Secondly, for the defects of fuzzy control summary time is too long, the immunity algorithm with excellent feedback is combined with fuzzy control to realize more accurate control of the controlled object with less summary time, which improves the efficiency and stability of the control system. Based on the generalized Routh criterion, the theoretical determination steps of the stability and convergence of the intelligent controller in this paper are given to provide a guarantee for the safe operation of the intelligent control system. The output trajectory oscillation range of the controller based on fuzzy immune PID algorithm designed in this paper is within -5~1, which is smaller than that of the traditional controller output trajectory. And the optimization algorithm in this paper has a very small error in tracking maximum power in a variety of local shading environments, and the oscillation range in dynamic environment is within 0.4W, which are smaller than the oscillation interval under the fuzzy control algorithm and immune algorithm alone. It shows that the optimized hybrid algorithm in this paper has important research significance for the development of intelligent control systems.

The ant colony optimization (ACO) algorithm is improved and further integrated with the immune algorithm (IA) to address its problems, such as slow convergence, local optimum, and premature convergence in the path planning. An algorithm integrating IA and improved ant colony optimization (IACO) is, therefore, put forward to realize the optimal planning of global path for an unmanned surface vehicle (USV). First, the ACO algorithm was improved in three aspects, that is, generation of initial pheromones, transition probability, and update of pheromones. The proposed IA-IACO algorithm combined the advantages of IA and IACO, sped up the convergence, and enhanced the optimization capability and operational efficiency. Second, the IA-IACO algorithm was designed and applied in the global path planning of an unmanned surface vehicle, achieving great global optimization and convergence. Finally, a path smoothing algorithm was devised to achieve the implementable, economic, and stable path while guaranteeing the safe navigation of the USV. A simulation test was carried out to prove the effectiveness and superiority of the designed global path planning algorithm in the practical engineering.

Based on the antibody clonal selection theory of immunology and differential evolution paradigm, we put forward a novel artificial immune system algorithm, differential immune clonal selection algorithm, termed as DICSA. The DICSA evolves and improves the population by clonal proliferation operator, differential mutation operator, differential crossover operator and (mu+lambda)-selection operator. In experiments, six composition functions are used to validate the performance of the DICSA. The results confirm its potential to solve the global optimization problems.

The mammalian immune system is a subject of great research interest because of its powerful information processing capabilities, namely, adaptivity. The adaptivity of the immune system is characterized by mainly two aspects, responsibility and diversity. The responsibility is a result of the response network of the immune system and the diversity is arise from gene rearrengement of the immune system. Recentry many artificial immune algorithms were devised by inspiring the adaptivity of the immune system. In terms of the two aspects of the immune system, however, those artificial immune algorithms only utilize the responsibility using models of response and regulation networks in the immune system. This paper proposes a new scheme of artificial immune algorithm, called Rearrangement Immune Algorithm (RIA), in which the rearrengement of the immune system is utilized combining evolution of the gelmline with an optimization of genetic algorithms. We empirically shows the effectiveness of our new scheme, RIA, with applying the Rosenbrock function and an HP folding problem.

Vehicle routing problem is a typical NP hard problem in combinatorial optimization. This paper proposes an improved algorithm, namely Immune Ant Colony Algorithm to solve the problem of vehicle routing problem. The algorithm is based on the intelligent optimization idea- Ant Colony Algorithm and Artificial Immune Algorithm. In this paper, in order to solve the problem of vehicle routing problem effectively four aspects are modified to improve the basic ant colony algorithm. And the improved Artificial Immune Algorithm is used to solve the problem of Ant Colony Algorithm to be apt to plunge into as the local optimum solution. On the basis of these improvements, the framework of the new algorithm—Immune Ant Colony Algorithm is given. Finally, it was proved that Immune Colony Algorithm can quicken the convergence rate to obtain optimal solution and decrease computing time. In addition, the computing simulation examples show its validity.

A novel hybrid immune algorithm and its convergence based on the steepest descent algorithm

This paper presents an immune PI control that consists of immune optimization algorithm and PI control. Traditional PI control can’t quickly match variation of motor variables or load disturbance in permanent magnet synchronous motor since control parameters are discrete and off-line adjusted. Therefore, by designing target function, antigen affinity, antibodies affinity and mutation, the immune algorithm optimizes parameters and memory cells. The tests and the simulation with time-based Parameters demonstrate immune algorithm makes control strategy and has global optimization ability and strong robustness. Based on DSP technology and SVPWM algorithm, the system shows that immune PI control system can obtain stable situation in 50ms, have much improvement in dynamic and static performance.

Intelligent parking systems have been recognized as a core technological intervention for resolving parking garage shortages and advancing traffic safety. Nevertheless, it remains challenging to generate a smooth, accurate, and optimal parking trajectory when employing conventional intelligent path optimization algorithms. Hence, building upon a newly designed optimization model for intelligent vehicle parking path planning, this study develops an improved immune moth-flame optimization algorithm (IIMFO). Specifically, aiming at the shortest path length and smooth enough trajectory, we leverage a cubic spline interpolation-driven path planning model to resolve the complex automatic parking trajectory optimization problem. To significantly strengthen the optimization effect, we introduce immune concentration selection, nonlinear decaying adaptive inertia weight adjustments, and elite opposition-based learning mechanisms to improve the immune moth-flame algorithm. Based on the evaluation results of the test functions, as well as the simulation and semi-automatic experiments of the real-world scenario of intelligent vehicle parking path optimization, the results indicate that the improved strategy can achieve better parking trajectories.

In order to reduce tire tread pattern noise, an adaptive immune genetic algorithm (AIGA) is presented to optimize tire pitch parameters in this paper. According to character of tire pattern, the algorithm defines crossover, mutation and reverse order operation to improve searching ability. The multiple parameters optimization is discussed in this paper. The simulation results indicate that compared with genetic algorithm (GA) and immune genetic algorithm (IGA), the convergence and the efficiency of AIGA are distinctly improved. The optimized results can reduce tread patterns noise level, which has been applicable to development of tire thread pattern.