Artificial Immune System Optimization Research Articles

Solving Travelling Salesman Problem using Artificial Immune System Optimization (AISO)

Travelling Salesman Problem (TSP) is a typical NP complete combinatorial optimization problem with various applications. In this paper, a nature inspired meta-heuristic optimization algorithm named as Artificial Immune System Optimization (AISO) algorithm is proposed for solving TSP. There are other approaches for solving this problem, namely Greedy Method, Brunch and Bound (B&B), and Dynamic Programming (DP) but they are not very efficient. The time complexity of Greedy approach is O (n2). However, the Greedy method doesn't always converge to an optimum solution whereas the B&B increases search space exponentially and DP finds out optimal solution in O (n22 n) time. The population based meta-heuristic optimization algorithms such as Artificial Immune System Optimization (AISO) and Genetic Algorithm (GA) provide a way to find solution of the TSP in linear time complexity. The proposed algorithm finds out the best cell (optimum solution) using a Survivor Selection (SS) operator which reduces the search space to ensure that effective information is not lost. Dataset, results and convergence graphs are presented and accuracy of the analysis is briefly discussed.

Optimization of Reliability–Redundancy Allocation Problems: A Review of the Evolutionary Algorithms

The study of optimization methods for reliability–redundancy allocation problems is a constantly changing field. New algorithms are continually being designed on the basis of observations of nature, wildlife, and humanity. In this paper, we review eight major evolutionary algorithms that emulate the behavior of civilization, ants, bees, fishes, and birds (i.e., genetic algorithms, bee colony optimization, simulated annealing, particle swarm optimization, biogeography-based optimization, artificial immune system optimization, cuckoo algorithm and imperialist competitive algorithm). We evaluate the mathematical formulations and pseudo-codes of each algorithm and discuss how these apply to reliability–redundancy allocation problems. Results from a literature survey show the best results found for series, series–parallel, bridge, and applied case problems (e.g., overspeeding gas turbine benchmark). Review of literature from recent years indicates an extensive improvement in the algorithm reliability performance. However, this improvement has been difficult to achieve for high-reliability applications. Insights and future challenges in reliability–redundancy allocation problems optimization are also discussed in this paper.

Pengenalan Pola Angka Menggunakan Pendekatan Optimisasi Sistem Kekebalan Buatan (Artificial Immune System)

The general election in Indonesia itself still experiences technical and non-technical problems where the technical problems occur in the recapitulation of votes from sheet C1 which are still incorrectly inputted and done manually. The problem occurred with the difference in the uploaded C1 data and the data in the KPU Situng and the C1 sheet uploaded was blurry, unclear, sheet C1 which was crossed out or folded in the KPU Situng. The purpose of this research is to reduce errors in data input and change the work that is done manually to the system, create a number pattern recognition system using an Artificial Immune System optimization approach, test and analyze the work of the system by taking into account the level of accuracy, preciseness and speed in recognize number patterns. The system created to applies an artificial immune system optimization approach with the Artificial Immune System using the Randomized Real-Valued Negative Selection Algorithm algorithm.

Optimal scheduling-based RFID reader-to-reader collision avoidance method using artificial immune system

In radio frequency identification (RFID) systems, communication signals from one desired reader is subject to interference from the other adjacent readers operating at the same time, so the reader-to-reader collision problem occurs. Many RFID reader collision avoidance methods have been developed, such as coverage-based methods, control mechanism-based methods and scheduling-based methods. In the scheduling-based methods, how to allocate frequency channels and time slots for the RFID reader network is emphasized. In this case, the RFID reader collision avoidance problem is transferred as an optimal scheduling problem, which can be solved by analytical methods and intelligent algorithms. Artificial Immune System (AIS) optimization is an emerging heuristic method derived from the human immune system. Due to its powerful global searching capability, AIS has been widely applied to scientific and engineering problems. This paper attempts to formulate the reader-to-reader collision problem (R2RCP) and its scheduling-based reader-to-reader collision avoidance model (R2RCAM), and proposes an improved AIS optimization for resource allocation (RA-AIS) in R2RCAM. Within the proposed RA-AIS optimization, the candidate antibody is constructed by using frequency channels and time slots, and in the mutation phase, the candidate antibody evolves dynamically according to its corresponding readers’ interfering power. The proposed RA-AIS optimization is examined on a series of numerical experiments to evaluate the effects of time slots, frequency channels, and transmitting power. Moreover, a group of comparative experiments are also arranged. The experimental results demonstrate that the proposed RA-AIS optimization is an effective method for R2RCAM, and performs better in searching the maximum interrogation area than other algorithms, such as random method (RM), genetic algorithm (GA), particle swarm optimization (PSO) and the canonical artificial immune system optimization (opt-aiNet).

Methodology for robust monitoring of voltage sags based on equipment trip probabilities

This paper presents a new methodology for installing monitors into a distribution network to optimally and robustly monitor the voltage sag performance of the entire network. The developed methodology allows distribution network planners to specify budgetary requirements and future loading forecasts and output a robust set of monitoring locations for voltage sag performance monitoring. The research utilizes an artificial immune system optimization (AIS) algorithm to discover a range of near-optimal monitoring locations based on minimization of expected equipment trips at a bus. An estimate for the probable number of equipment trips is defined using a new probabilistic measure known as sag trip probability (STP). The optimized solutions are analyzed for robustness across a range of uncertain future network loading and topology scenarios. The results show the methodology applied to a 295 bus generic distribution network with uncertain load growth for the next 15 years.

Automatic point correspondence using an artificial immune system optimization technique for medical image registration

In this paper, an automatic method for determining pairs of corresponding points between medical images is proposed. The method is based on the implementation of an artificial immune system (AIS). AIS is a relatively novel, population based category of algorithms, inspired by theoretical immunologic models. When used as function optimizers, AIS have the attractive property of locating the global optimum of a function as well as a large number of strong local optimum points. In this work, AIS has been applied both for the extraction of an optimal set of candidate points on the reference image and the definition of their corresponding ones on the second image. The performance of the proposed AIS algorithm is evaluated against the widely used Iterative Closest Point (ICP) algorithm in terms of the accuracy of the obtained correspondences and in terms of the accuracy of the point-based registration by the two correspondence algorithms and the Mutual Information criterion, as an intensity-based registration method. Qualitative and quantitative results involving 92 X-ray dental and 10 retinal image pairs subject to known and unknown transformations are presented. The results indicate a superior performance of the proposed AIS algorithm with respect to the ICP algorithm and the Mutual Information, in terms of both correct correspondence and registration accuracy.

Efficient combined immune-decomposition algorithm for optimal buffer allocation in production lines for throughput and profit maximization

Adequate allocation of buffers in transfer lines is crucial to the optimization of line throughput and work in process (WIP) inventory. Their optimal allocation is subject to specific constraints, associated costs, and revenue projections. In this paper, we implement a combined artificial immune system optimization algorithm in conjunction with a decomposition method to optimally allocate buffers in transfer lines. The aim of the buffer allocation problem (BAP) is to achieve optimal system performance under buffers space constraints. Maximizing line throughput does not necessarily achieve maximum profit. In this study the immune decomposition algorithm (IDA) is used to determine optimal buffer allocation for maximum line throughput and maximum line economic profit. Results of extensive series of tests carried out to compare, in production lines with different characteristics, the performances of the proposed method and those of other algorithms are presented.

Design technology, computer science, and CIM implementation

This paper presents a new methodology for installing monitors into a distribution network to optimally and robustly monitor the voltage sag performance of the entire network. The developed methodology allows distribution network planners to specify budgetary requirements and future loading forecasts and output a robust set of monitoring locations for voltage sag performance monitoring. The research utilizes an artificial immune system optimization (AIS) algorithm to discover a range of near-optimal monitoring locations based on minimization of expected equipment trips at a bus. An estimate for the probable number of equipment trips is defined using a new probabilistic measure known as sag trip probability (STP). The optimized solutions are analyzed for robustness across a range of uncertain future network loading and topology scenarios. The results show the methodology applied to a 295 bus generic distribution network with uncertain load growth for the next 15 years.