Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator

The issue of optimization of the configuration and operating states in low voltage microgrids is important both from the point of view of the proper operation of the microgrid and its impact on the medium voltage distribution network to which such microgrid is connected. Suboptimal microgrid configuration may cause problems in networks managed by distribution system operators, as well as for electricity consumers and owners of microsources and energy storage systems connected to the microgrid. Structures particularly sensitive to incorrect determination of the operating states of individual devices are hybrid microgrids that combine an alternating current and direct current networks with the use of a bidirectional power electronic converter. An analysis of available literature shows that evolutionary and swarm optimization algorithms are the most frequently chosen for the optimization of power systems. The research presented in this article concerns the assessment of the possibilities of using artificial immune systems, operating on the basis of the CLONALG algorithm, as tools enabling the effective optimization of low voltage hybrid microgrids. In his research, the author developed a model of a hybrid low voltage microgrid, formulated three optimization tasks, and implemented an algorithm for solving the formulated tasks based on an artificial immune system using the CLONALG algorithm. The conducted research consisted of performing a 24 h simulation of microgrid operation for each of the formulated optimization tasks (divided into 10 min independent optimization periods). A novelty in the conducted research was the modification of the hypermutation operator, which is the key mechanism for the functioning of the CLONALG algorithm. In order to verify the changes introduced in the CLONALG algorithm and to assess the effectiveness of the artificial immune system in solving optimization tasks, optimization was also carried out with the use of an evolutionary algorithm, commonly used in solving such tasks. Based on the analysis of the obtained results of optimization calculations, it can be concluded that the artificial immune system proposed in this article, operating on the basis of the CLONALG algorithm with a modified hypermutation operator, in most of the analyzed cases obtained better results than the evolutionary algorithm. In several cases, both algorithms obtained identical results, which also proves that the CLONALG algorithm can be considered as an effective tool for optimizing modern power structures, such as low voltage microgrids, including hybrid AC/DC microgrids.

Feature Level Fusion of Biometric Images Using Modified Clonal Selection Algorithm

In feature level fusion, biometric features must be combined such that each trait is combined so as to maintain feature-balance. To achieve this, Modified Clonal Selection Algorithm was employed for feature level fusion of Face, Iris and Fingerprints. Modified Clonal Selection Algorithm (MCSA) which is characterized by feature-balance maintenance capability and low computational complexity was developed and implemented for feature level fusion. The standard Tournament Selection Method (TSM) was modified by performing tournaments among neighbours rather than by random selection to reduce the between-group selection pressure associated with the standard TSM. Clonal Selection algorithm was formulated by incorporating the Modified Tournament Selection Method (MTSM) into its selection phase. Quantitative experimental results showed that the systems fused with MCSA has a higher recognition accuracy than those fused with CSA, also with a lower recognition time. Keywords: Biometrics, Feature level Fusion, Multibiometrics, Modified Clonal Selection Algorithm, Recognition Accuracy, Recognition Time.

CAN LINEAR PROGRAMMING ASSIST METAHEURISTICS IN FOREST PRODUCTION PLANNING PROBLEM?

The planning of forest production requires the adoption of mathematical models to optimize the utilization of available resources. Hence, studies involving the improvement of decision-making processes must be performed. Herein, we evaluate an alternative method for improving the performance of metaheuristics when they are applied for identifying solutions to problems in forest production planning. The inclusion of a solution obtained by rounding the optimal solution of linear programming to a relaxed problem is investigated. Such a solution is included in the initial population of the clonal selection algorithm, genetic algorithm, simulated annealing, and variable neighborhood search metaheuristics when it is used to generate harvest and planting plans in an area measuring 4,210 ha comprising 120 management units with ages varying between 1 and 6 years. The same algorithms are executed without including the solutions mentioned in the initial population. Results show that the performance of the clonal selection algorithm, genetic algorithm, and variable neighborhood search algorithms improved significantly. Positive effects on the performance of the simulated annealing metaheuristic are not indicated. Hence, it is concluded that rounding off the solution to a relaxed problem is a good alternative for generating an initial solution for metaheuristics.

An artificial immune system algorithm for solving the uncapacitated single allocation p-Hub median problem

The present paper deals with a variant of hub location problems (HLP): the uncapacitated single allocation p-Hub median problem (USApHMP). This problem consists to jointly locate hub facilities and to allocate demand nodes to these selected facilities. The objective function is to minimize the routing of demands between any origin and destination pair of nodes. This problem is known to be NP-hard. Based on the artificial immune systems (AIS) framework, this paper develops a new approach to efficiently solve the USApHMP. The proposed approach is in the form of a clonal selection algorithm (CSA) that uses appropriate encoding schemes of solutions and maintains their feasibility. Comprehensive experiments and comparison of the proposed approach with other existing heuristics are conducted on benchmark from civil aeronautics board, Australian post, PlanetLab and Urand data sets. The results obtained allow to demonstrate the validity and the effectiveness of our approach. In terms of solution quality, the results obtained outperform the best-known solutions in the literature.

Estimation of parameters in groundwater modelling by modified Clonalg

The purpose of this study was to improve the optimization model for predicting more parameters in more difficult conditions (more grid cell numbers and high time interval numbers) than other studies in groundwater flow modelling. Also, the model needs fewer observation numbers for estimating parameters than other studies. In the present study, an optimization model based on model calibration was developed to estimate simultaneously four groundwater flow parameters – hydraulic conductivity, transmissivity, storage coefficient and leakance. The modified clonal selection algorithm, a class of artificial immune systems, was used as a heuristic optimization method. In order to simulate the groundwater flow, MODFLOW was used in conjunction with the model in MATLAB. The input files for MODFLOW were obtained by GMS groundwater simulator. The model was applied to two different hypothetical groundwater systems (two- and three-dimensional) under transient conditions to evaluate its performance. The results showed that the model was feasible for groundwater flow modelling and it could determine the groundwater flow parameters successfully with less observations and more grid cell numbers than the other studies.

Artificial Immune System Applied to Job Shop Scheduling

Job Shop Scheduling is a problem to schedule n number of jobs in m number of machines with a different order of processing. Each machine processes exactly one job at a time. Each job will be processed in every machine once. When a machine is processing one particular job then the other machine can’t process the same job. Different schedule’s order might produce different total processing time. The result of this scheduling problem will be total processing time and schedule’s order. This paper uses clonal selection as the algorithm to solve this problem. The clonal selection algorithm comes from the concept of an artificial immune system. It's developed by copying a human’s immune system behavior. A human’s immune system can differentiate foreign objects and eliminate the objects by creating an antibody. An antibody will go to a cloning process and will mutate to further enhance itself. Clonal selection algorithm applies this cloning and mutation principle to find the most optimal solution. The goal is to find the best schedule’s order and makespan. Taillard’s benchmark is used to verify the quality of the result. To compare the result, we use two values: the upper bound and the lower bound. The upper bound is used to describe the best result of a scheduling problem that has been conducted using a certain environment. On the contrary, the lower bound shows the worst. Experiments on changing the algorithm's parameters are also conducted to measure the quality of the program. The parameters are the number of iterations, mutations, and clone numbers. According to the experiment's results, the higher the number of iteration, mutation rate, and clone number, the better solution for the problem. Clonal selection algorithm has not been able to keep up with upper bound or lower bound values from Taillard’s case. Therefore, parameters need to be increased significantly to increase the chance to produce the optimum result. The higher number of parameters used means the longer time needed to produce the result.

A New Artificial Immune System Based on Continuous Learning for Pattern Recognition

This paper presents a novel approach for pattern recognition based on continuous training inspired by the biological immune system operation. The main objective of this paper is to present a method capable of continually learn, i.e., being able to address new types of patterns without the need to restart the training process (artificial immune system with incremental learning). It is a useful method for solving problems involving a permanent knowledge extraction, e.g., 3D facial expression recognition, whose quality of the solutions is strongly dependent on a continuous training process. In this context, two artificial immune algorithms are employed: (1) the negative selection algorithm, which is responsible for the pattern recognition process and (2) the clonal selection algorithm, which is responsible for the learning process. The main application of this method is in assisting in decision-making on problems related to pattern recognition process. To evaluate and validate the efficiency of this method, the system has been tested on handwritten character recognition, which is a classic problem in the literature. The results show efficiency, accuracy and robustness of the proposed methodology.