Artificial Immune System based on Hybrid and External Memory for Mathematical Function Optimization

Artificial immune system (AIS) is one of the natureinspired algorithm for optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be further improved because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. Thus, a hybrid PSO-AIS and a new external memory CSA based scheme called EMCSA are proposed. In hybrid PSO-AIS, the good features of PSO and AIS are combined in order to reduce any limitation. Alternatively, EMCSA captures all the best antibodies into the memory in order to enhance global searching capability. In this preliminary study, the results show that the performance of hybrid PSO-AIS compares favourably with other algorithms while EMCSA produced moderate results in most of the simulations

Artificial Immune System Based Remainder Method for Multimodal Mathematical Function Optimization

Artificial immune system (AIS) is one of the nature-inspired algorithm for solving optimization problems. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability compare to other meta-heuristic methods. However, the CSA rate of convergence and accuracy can be further improved as the hypermutation in CSA itself cannot always guarantee a better solution. Conversely, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have an inclination to converge prematurely. In this work, the CSA is modified using the best solutions for each exposure (iteration) namely Single Best Remainder (SBR) - CSA. Simulation results show that the proposed algorithm is able to enhance the performance of the conventional CSA in terms of accuracy and stability for single objective functions

Antibody Remainder Method Based Artificial Immune System for Mathematical Function Optimization

Artificial immune system (AIS) is one of the natureinspired algorithm for solving optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be improved further because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively,Genetic Algorithms (GAs) and Particle Swarm Optimization(PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. In this study, the CSA is modified using the best solution for each exposure (iteration) namely Single Best Remainder (SBR) CSA. In this study, the results show that the performance of the proposed algorithm (SBR-CSA) compares favourably with other algorithms while Half Best Insertion (HBI) CSA produced moderate results in most of the simulations

Aikido: Accelerating shared data dynamic analyses

Despite a burgeoning demand for parallel programs, the tools available to developers working on shared-memory multicore processors have lagged behind. One reason for this is the lack of hardware support for inspecting the complex behavior of these parallel programs. Inter-thread communication, which must be instrumented for many types of analyses, may occur with any memory operation. To detect such thread communication in software, many existing tools require the instrumentation of all memory operations, which leads to significant performance overheads. To reduce this overhead, some existing tools resort to random sampling of memory operations, which introduces false negatives. Unfortunately, neither of these approaches provide the speed and accuracy programmers have traditionally expected from their tools. In this work, we present Aikido, a new system and framework that enables the development of efficient and transparent analyses that operate on shared data. Aikido uses a hybrid of existing hardware features and dynamic binary rewriting to detect thread communication with low overhead. Aikido runs a custom hypervisor below the operating system, which exposes per-thread hardware protection mechanisms not available in any widely used operating system. This hybrid approach allows us to benefit from the low cost of detecting memory accesses with hardware, while maintaining the word-level accuracy of a software-only approach. To evaluate our framework, we have implemented an Aikido-enabled vector clock race detector. Our results show that the Aikido enabled race-detector outperforms existing techniques that provide similar accuracy by up to 6.0x, and 76% on average, on the PARSEC benchmark suite.National Science Foundation (U.S.) (NSF grant CCF-0832997)National Science Foundation (U.S.) (DOE SC0005288)United States. Defense Advanced Research Projects Agency (DARPA HR0011-10- 9-0009

Mathematical function optimization using AIS antibody remainder method

Artificial immune system (AIS) is one of the metaheuristics used for solving combinatorial optimization problems. In AIS, clonal selection algorithm (CSA) has good global searching capability. However, the CSA convergence and accuracy can be improved further because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. In this study, the CSA is modified using the best solutions for each exposure (iteration) namely Single Best Remainder (SBR) - CSA. The results show that the proposed algorithm is able to improve the conventional CSA in terms of accuracy and stability for single and multi objective functions

An Improved Artificial Immune System Based On Antibody Reminder Method For Mathematical Function Optimization

Artificial immune system (AIS) is one of the nature inspired algorithm for optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be improved further because the hyper mutation in CSA itself cannot always guarantee a better solution. Alternatively, Genetic Algorithms (GAS) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. I n this study, the CSA is modified using the best solutions for each exposure (iteration) namely Remainder-CSA. The results show that the proposed algorithm is able to improve the conventional CSA in terms of accuracy and stability for single objective functions

Particle Swarm based Artificial Immune System for Multimodal Function Optimization and Engineering Application Problem

Artificial Immune Systems (AIS) has generated great interest among researchers as the algorithm is able to improve local searching ability and efficiency. However, the rate of convergence for AIS in finding the global minima is rather slow as compare to other Evolutionary Algorithms. Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used effectively in solving complicated optimization problems, but they tend to converge prematurely at the local minima. In this study, the hybrid AIS (HAIS) is proposed by combining the good features of AIS and PSO in order to reduce this shortcoming. By comparing the optimization results of the mathematical functions and the engineering problem using GA, AIS and HAIS, it is observed that HAIS achieved better performances in terms of accuracy, convergence rate and stability

Mapping localized surface plasmons within silver nanocubes using cathodoluminescence hyperspectral imaging

Localized surface plasmons within silver nanocubes less than 50 nm in size are investigated using high resolution cathodoluminescence hyperspectral imaging. Multivariate statistical analysis of the multidimensional luminescence dataset allows both the identification of distinct spectral features in the emission and the mapping of their spatial distribution. These results show a 490 nm peak emitted from the cube faces, with shorter wavelength luminescence coming from the vertices and edges; this provides direct experimental confirmation of theoretical predictions

Artificial Immune Algorithm Based Gravimetric Fluid Dispensing Machine

One of the most prominent methods used in handling the end process for materials-mixing is by having a dispensing system. An effective dispensing method using Pulse Width Modulation (PWM) at the end of the dispensing sequence with Artificial Immune System (AIS) automatic dispensing parameter fine tuning capability is proposed by optimizing the components of Dispensing Time and Stopping Time Delay to obtain constant and accurate reading from the precision balance scale. Based on the new dispensing sequence, experimental tests had been carried out using different materials with varying viscosities. The results denote that the combination of both PWM and AIS techniques would minimize the error rate for overshooting while exhibiting better accuracy. These are important in order to overcome the limitations of the conventional volumetric dispensing and manual parameter tuning presently applied in the dispensing system used in the coatings industry