Gradient-based immune algorithm for optimization of dynamic environments

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.

This paper introduces the optimization of a quantum control application, the so-called molecular alignment problem, subject to a dynamic environment. Given the relative simplicity of optimized pulse-shapes in the low-intensity variant of the problem, versus the high complexity of the optimized pulse-shapes in the high-intensity case, a dynamic-intensity environment is simulated in a noise-free calculation. Specific evolution strategies, natural candidates for optimization in dynamic environments, are applied to this task. The calculations reveal the evolution of the pulse-shapes and their underlying evolving structures, that allow a complete physical interpretation. The combination of an optimization in a dynamic environment with the examination of the intermediate optimized solutions offers a sharper physics view of the problem, and accomplishes a fruitful interdisciplinary study.

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.

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.

Immune Evolutionary Algorithm (IEA) is proposed on the shortages of evolution algorithm and biological immune mechanism. According to the characteristics of chaos, a novel Chaotic Immune Evolutionary Algorithm (CIEA) is presented which introduces chaos to IEA. The algorithm has the merits of chaos, immunity and evolutionary algorithm. It can ensure the ability of global search and local search and enhance the performances of the algorithm. At last, we analyze the efficiency of the algorithm with two typical optimization problems. The analysis result shows that CIEA converges quickly and effectively avoids the inherent problem that the evolution algorithm traps in immature convergence, so CIEA is an effective way to solve complex optimization problem.

Conventional evolutionary algorithms operate in a fixed search space with limiting parameter range, which is often predefined via a priori knowledge or trial and error in order to `guess' a suitable region comprising the global optimal solution. This requirement is hard, if not impossible, to fulfil in many real-world optimization problems since there is often no clue of where the desired solutions are located in these problems. Thus, this paper proposes an inductive---deductive learning approach for single- and multi-objective evolutionary optimization. The method is capable of directing evolution towards more promising search regions even if these regions are outside the initial predefined space. For problems where the global optimum is included in the initial search space, it is capable of shrinking the search space dynamically for better resolution in genetic representation to facilitate the evolutionary search towards more accurate optimal solutions. Validation results based on benchmark optimization problems show that the proposed inductive---deductive learning is capable of handling different fitness landscapes as well as distributing nondominated solutions uniformly along the final trade-offs in multi-objective optimization, even if there exist many local optima in a high-dimensional search space or the global optimum is outside the predefined search region.

A prediction mechanism for Memetic Algorithm is presented in this paper. The Predictive Memetic Algorithm (PMA) uses a nonlinear regression method to estimate the parameters used by the algorithm to obtain good solutions in a dynamic and stochastic environment. The algorithm is applied to nonlinear data sets and performance is compared with genetic and simulated annealing algorithms. When compared with the existing methods, the proposed method generates a relatively small error difference after prediction thereby proving its superior performance. A dynamic stochastic environment is used for experimentation, so as to determine the efficacy of the algorithm on non-stationary problem environments.

A novel immune algorithm is suggested for finding Pareto-optimal solutions to multiobjective optimization problems based on opt-aiNET, the artificial immune system algorithm for multi-modal optimization. In the proposed algorithm, a randomly weighted sum of multiple objectives is used as a fitness function, and a local search algorithm is incorporated to facilitate the exploitation of the search space. Specifically, a new truncation algorithm with similar individuals (TASI) is proposed to preserve the diversity of the population. Also, a new selection operator is presented to create the new population based on TASI. Simulation results on seven standard problems (ZDT2, ZDT6, DEB, VNT, BNH, OSY and KIT) show that the proposed algorithm is able to find a much better spread of solutions and better convergence near the true Pareto-optimal front compared to the vector immune algorithm and the elitist non-dominated sorting genetic system.

Based on the polar coordination particle swarm optimization (PPSO), this paper presents a novel method for the robot path planning in dynamic environment. It decomposes the task into a global planning stage and a local planning stage. PPSO algorithm can search for the global optimal path based on static obstacles information. When the robot moves along the optimal global path, an on-line real-time path planning strategy is adopted to avoid dynamic obstacles by means of predicting the future positions of moving obstacles. Simulation experiment shows that the method is more efficient than traditional particle swarm optimization (TPSO) and genetic algorithm (GA) for solving path planning problem. The feasibility and high stability of real-time obstacle avoidance strategy are demonstrated in dynamic environment.

A model of job shop scheduling with multi-resource constraints is studied in this paper, which fully considers the dynamic condition, flexible process plan, non-cutting time of the process, the availability of resource(machine/fixture) and the batch size of job based on practical production condition. IDEF0 and object-oriented technology are used to build the complicated model. The functional model of the job shop scheduling system is built with IDEF0. Different object classes are defined with object-oriented technology. An association model of these classes is built, and the information model of the scheduling system is formed. Then an immune algorithm based on the biological immunity mechanism is used to solve this problem. Finally an illustrative example is tested on the software programmed. The results show that the model and the immune algorithm proposed can solve the complex job shop scheduling problem with multi-resource constraints effectively.

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.

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.

In the real world, many optimization problems are dynamic. This requires an optimization algorithm to not only find the global optimal solution under a specific environment but also to track the trajectory of the changing optima over dynamic environments. To address this requirement, this paper investigates a clustering particle swarm optimizer (PSO) for dynamic optimization problems. This algorithm employs a hierarchical clustering method to locate and track multiple peaks. A fast local search method is also introduced to search optimal solutions in a promising subregion found by the clustering method. Experimental study is conducted based on the moving peaks benchmark to test the performance of the clustering PSO in comparison with several state-of-the-art algorithms from the literature. The experimental results show the efficiency of the clustering PSO for locating and tracking multiple optima in dynamic environments in comparison with other particle swarm optimization models based on the multiswarm method.

The present study evaluated the diagnostic accuracy of immune system algorithms with the aim of classifying the primary types of headache that are not related to any organic etiology. They are divided into four types: migraine, tension, cluster, and other primary headaches. After we took this main objective into consideration, three different neurologists were required to fill in the medical records of 850 patients into our web-based expert system hosted on our project web site. In the evaluation process, Artificial Immune Systems (AIS) were used as the classification algorithms. The AIS are classification algorithms that are inspired by the biological immune system mechanism that involves significant and distinct capabilities. These algorithms simulate the specialties of the immune system such as discrimination, learning, and the memorizing process in order to be used for classification, optimization, or pattern recognition. According to the results, the accuracy level of the classifier used in this study reached a success continuum ranging from 95% to 99%, except for the inconvenient one that yielded 71% accuracy.

To solve the real-time path planning of multi-robots in complex environments, a new immune planning algorithm incorporating a specific immune mechanism is presented. In the immune planning algorithm incorporating a specific immune mechanism, a new coding format for an antibody is first defined according to the impact of the obstacle distribution on the obstacle avoidance behaviors of multi-robots. Then, a new robot immune dynamic model for antibody selection is designed in terms of different impacts of obstacles and targets on robot behaviors. Finally, aiming at the local minimum problem in complex environments and inspired by the specific immune mechanism, a series of appropriate avoidance behaviors are selected through the calculation of a specific immune mechanism to help robots walk out of local minima. In addition, to solve deadlock situations, a learning strategy for the antibody concentration is presented. Compared with four related immune planning algorithms—an improved artificial potential field, a rapidly exploring random tree algorithm, a D* algorithm and a A* algorithm—the simulation results in four static environments show that the paths planned by immune planning algorithm incorporating a specific immune mechanism are the shortest and the path smoothness is generally the highest, which shows its strong planning capability in multi-obstacle environments. The simulation result in a dynamic environment with local minima shows that the immune planning algorithm incorporating a specific immune mechanism has strong planning ability in dynamic obstacle avoidance and in escaping from local minima. Additionally, an experiment in a multi-robot environment shows that two robots can not only avoid static obstacles but also avoid dynamic obstacles, which further supports the validity of the proposed immune planning algorithm incorporating a specific immune mechanism for multi-robots in real environments.