Papers by Keyword: Artificial Immune System

Abstract: The design of gears is critical for smooth running of any mechanism, automobile and machinery. Gear drive design starts with the need of optimizing the gear thickness, module, number of teeth etc., this creates huge challenges to a designer. Optimization algorithms are more flexible and gaining importance in engineering design problems, because of the accessibility and affordability of today’s mechanical field. A population based heuristic algorithm offers well-organized ways of creating and comparing a novel design solution in order to complete an optimal design. In this paper, a new artificial immune system based algorithm proposed as Modified Artificial Immune System (MAIS) algorithm is used to optimize a gear design problem. The results are compared with an existing design.

Abstract: The classical job-shop scheduling problem is one of the most difficult combinatorial optimization problems. Scheduling is defined as the art of assigning resources to tasks in order to insure the termination of these tasks in a reasonable amount of time. Job shop scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Mathematical and heuristic methods are the two major methods for resolving JSP. Job shop Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions. In this paper, a Hybrid algorithm combined artificial immune system and sheep flock heredity model algorithm is used for minimizing the total holding cost for different size benchmark problems. The results show that the proposed hybrid algorithm is an effective algorithm that gives better results than other hybrid algorithms compared in literature. The proposed hybrid algorithm is a good technique for scheduling problems.

Abstract: This paper proposes an artificial immune system for the economic environmental dispatch (EED) of the hydrothermal power system (HPS), considering non-smooth fuel cost and emission level functions. The artificial immune system (AIS) equipped with an accelerated operation and a migration operation can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to handle the equality and inequality constraints of the HPS, and the ε-constraint technique is employed to manage the constraints of HPS. To show the advantages of the proposed algorithm, one example addressing the best compromise is applied to test the EED problem of the HPS. The proposed algorithm integrates the AIS, the MU and the ε-constraint technique, revealing that the proposed approach has the following merits – 1)ease of implementation; 2)applicability to non-smooth fuel cost and emission level functions; 3)better effectiveness than the previous method, and 4)the requirement for only a small population in applying the optimal EED problem of the HPS.

Abstract: Problem of multi-objective optimization based on Artificial Immune System (AIS) is an important research area of current evolutionary computing. Starting from the intelligent information processing mechanism of immune theory and the immune system itself, a kind of evolutionary multi-objective optimization algorithm based on AIS is proposed. Clonal selection, scattered crossover and hypermutation based on the learning mechanism are characteristics of the algorithm. Algorithm implements clonal selection according to the distribution of individuals in the objective space, which benefit obtaining Pareto optimal boundary distributed more widely and speed up the convergence. Compared with the existing algorithms, the algorithm has been greatly improved in convergence, diversity, and distribution of solutions.

Abstract: Problem of multi-objective optimization based on Artificial Immune System (AIS) is an important research area of current evolutionary computing. Starting from the intelligent information processing mechanism of immune theory and the immune system itself, by researching the calculating model of immune evolution and Based on the biological mechanism of immune system, a general algorithm frame for solving optimization problem is proposed. The researching content has good theoretical value and practicability, also provides base for further research of specific type of problems.

Abstract: nspired by the theory of biological immune receptor editing/revision, an improved artificial immune system model is proposed. Different from generic model, the improved model does not need to set the detectors detection radius, but it gives the detector a certain degree of learning ability through receptor editing and receptor revision. This makes the detector has a capability to adjust the detection position and detection radius automatically. Experimental results show that the improved model achieves better detection performance than generic model.

Abstract: This paper proposes an artificial immune system with a multiplier updating method (AIS-MU) for multiple-fuel-constrained generation scheduling of power systems. The artificial immune system (AIS) equips with a migration can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to avoid deforming the augmented Lagrange function and resulting in difficulty of solution searching. The proposed method integrates the AIS and the MU that has merits of automatically adjusting the randomly given penalty to a proper value and requiring only a small-size population for the economic dispatch problem (EDP) of the multiple-fuel-constrained generation scheduling. Numerical results indicate that the proposed algorithm is more suitable than previous approaches in the practical economic dispatch of power system.

Abstract: A hybrid AIS-GA was proposed and tested. The algorithm performed very well in problems presenting continuous, discrete, and mixed design variables, producing feasible solutions in all runs for all problems considered. Also, it is much more easily parallelizable than the previous hybrids, and does not require any user-defined parameter other than the parameters already used by the AIS and the GA.

Abstract: In this paper, we present an oriented clonal selection algorithm (O-CLONALG) for mining association rules effectively for classification. Different with the traditional evolutionary algorithms, O-CLONALG firstly scans dataset one time to find the frequent rules with one item. The items are used to generate new rules and the mutation operation is limited in it. When mutation operation takes place, each rule in the same generation was added a new item. The results have shown that it is efficient in dealing with the problem on the complexity of the rule search space. At the same time, good classification accuracy has been achieved

Abstract: The aim of process mining is to identify and extract process patterns from data logs to reconstruct an overall process model. And the model’s structural complexity directly impacts readability and quality of the model. Immune systems have many characteristics such as uniqueness, autonomous, recognition of foreigners, distributed detection, and noise tolerance. This paper outlines an alternative approach to business process mining utilizing an artificial immune systems (AIS) technique, and some main steps and operators were depicted.