The algorithm development based on the immune search for solving unclear problems to detect the optical flow with minimal cost

The main aim of the research is the development of effective methods and algorithms based on the hybrid principles functioning of the immune system and evolutionary search to determine a global optimal solution to optimisation problems. Artificial immune algorithms are characterised as diverse ones, extremely reliable and implicitly parallel. The integration of modified evolutionary algorithms and immune algorithms is proposed to be used for the solution of above problem. There is no exact method for the efficient solving unclear optimisation problems within the polynomial time. However, by determining close to optimal solutions within the reasonable time, the hybrid immune algorithm (HIA) is capable to offer multiple solutions, which provide compromise between several goals. Quite few researches have been focused on the optimisation of more than one goal and even fewer used to have distinctly considered diversity of solutions that plays fundamental role in good performance of any evolutionary calculation method.

Diagnostic Accuracy Comparison of Artificial Immune Algorithms for Primary Headaches

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.

A Novel Quantum Immune Algorithm for Multiobjective Flexible Job Shop Scheduling

In this study, a hybrid of Quantum Evolutionary and Artificial Immune Algorithms (QIA) is proposed for solving Multiobjective Flexible Job Shop Scheduling Problem (MFJSSP). This problem is formulated as three-objective problem which minimizes completion time (makespan), critical machine workload and total work load of all machines. The quantum coding is shown to improve the immune strategy. The proposed algorithm overcomes the problem by increasing the speed of convergence and diversity of population. Three benchmarks of Kacem and Brandimart are examined to evaluate the performance of the proposed algorithm. The experimental results show a better performance in comparison to other approaches.

UNIFICATION AND DIVERSITY OF COMPUTATION MODELS FOR GENERALIZED SWARM INTELLIGENCE

Swarm intelligence is a kind of nature-inspired heuristic optimization technique. Different computation models usually take on relative uniform characteristic though they usually have distinct extrinsic forms. These intelligent algorithms are coupling with deterministic and stochastic, the contradiction between necessity and accidental unity, which promotes the "evolution" of the inheritance and the creative process: "stochastic" is adopted to give creative ability to the implemented "intelligent system", and a succession of "certainty" is acted to ensure the system is converging. In this paper, a computing framework of generalized swarm intelligence is proposed based on the unifying idea. The unified hiberarchy model and formalization description for swarm intelligence are represented. Several typical swarm intelligence algorithms, such as ant colony system (ACS), particle swarm optimization (PSO), estimation of distribution algorithms (EDA) and artificial immune algorithms (AIA) are addressed to validate the uniform idea of swarm intelligence, respectively.