Satisfiability Logic Analysis Via Radial Basis Function Neural Network with Artificial Bee Colony Algorithm

Radial Basis Function Neural Network (RBFNN) is a variant of artificial neural network (ANN) paradigm, utilized in a plethora of fields of studies such as engineering, technology and science. 2 Satisfiability (2SAT) programming has been coined as a prominent logical rule that defines the identity of RBFNN. In this research, a swarm-based searching algorithm namely, the Artificial Bee Colony (ABC) will be introduced to facilitate the training of RBFNN. Worth mentioning that ABC is a new population-based metaheuristics algorithm inspired by the intelligent comportment of the honey bee hives. The optimization pattern in ABC was found fruitful in RBFNN since ABC reduces the complexity of the RBFNN in optimizing important parameters. The effectiveness of ABC in RBFNN has been examined in terms of various performance evaluations. Therefore, the simulation has proved that the ABC complied efficiently in tandem with the Radial Basis Neural Network with 2SAT according to various evaluations such as the Root Mean Square Error (RMSE), Sum of Squares Error (SSE), Mean Absolute Percentage Error (MAPE), and CPU Time. Overall, the experimental results have demonstrated the capability of ABC in enhancing the learning phase of RBFNN-2SAT as compared to the Genetic Algorithm (GA), Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm

Hybrid Ant Colony Optimization For Two Satisfiability Programming In Hopfield Neural Network

The representation of 2 Satisfiability problem or 2SAT is increasingly viewed as a significant logical rule in order to synthesize many real life applications. Although there were many researchers proposed the solution of 2SAT, little attention has been paid to the significance of the 2SAT logical rule itself. It can be hypothesized that 2SAT property can be used as a logical rule in the intelligent system. To verify this claim, 2 Satisfiability logic programming was embedded to Hopfield neural network (HNN) as a single unit. Learning in HNN will be inspired by Wan Abdullah method since the conventional Hebbian learning is inefficient when dealing with large number of constraints. As the number of 2SAT clauses increased, the efficiency and effectiveness of the learning phase in HNN deteriorates. Swarm intelligence metaheuristic algorithm has been introduced to reduce the learning complexity of the network. The newly proposed metaheuristic algorithm was enhanced ant colony optimization (ACO) algorithm

Bee Colony Optimization - part II: The application survey

Bee Colony Optimization (BCO) is a meta-heuristic method based on foraging habits of honeybees. This technique was motivated by the analogy found between the natural behavior of bees searching for food and the behavior of optimization algorithms searching for an optimum in combinatorial optimization problems. BCO has been successfully applied to various hard combinatorial optimization problems, mostly in transportation, location and scheduling fields. There are some applications in the continuous optimization field that have appeared recently. The main purpose of this paper is to introduce the scientific community more closely with BCO by summarizing its existing successful applications. [Projekat Ministarstva nauke Republike Srbije, br. OI174010, OI174033, TR36002] Document type: Articl

Accelerating ant colony optimization by using local search

This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2015.Cataloged from PDF version of thesis report.Includes bibliographical references (page 42-45).Optimization is very important fact in terms of taking decision in mathematics, statistics, computer science and real life problem solving or decision making application. Many different optimization techniques have been developed for solving such functional problem. In order to solving various problem computer Science introduce evolutionary optimization algorithm and their hybrid. In recent years, test functions are using to validate new optimization algorithms and to compare the performance with other existing algorithm. There are many Single Object Optimization algorithm proposed earlier. For example: ACO, PSO, ABC. ACO is a popular optimization technique for solving hard combination mathematical optimization problem. In this paper, we run ACO upon five benchmark function and modified the parameter of ACO in order to perform SBX crossover and polynomial mutation. The proposed algorithm SBXACO is tested upon some benchmark function under both static and dynamic to evaluate performances. We choose wide range of benchmark function and compare results with existing DE and its hybrid DEahcSPX from other literature are also presented here.Nabila TabassumMaruful HaqueB. Computer Science and Engineerin

Major 3 Satisfiability logic in Discrete Hopfield Neural Network integrated with multi-objective Election Algorithm

Discrete Hopfield Neural Network is widely used in solving various optimization problems and logic mining. Boolean algebras are used to govern the Discrete Hopfield Neural Network to produce final neuron states that possess a global minimum energy solution. Non-systematic satisfiability logic is popular due to the flexibility that it provides to the logical structure compared to systematic satisfiability. Hence, this study proposed a non-systematic majority logic named Major 3 Satisfiability logic that will be embedded in the Discrete Hopfield Neural Network. The model will be integrated with an evolutionary algorithm which is the multi-objective Election Algorithm in the training phase to increase the optimality of the learning process of the model. Higher content addressable memory is proposed rather than one to extend the measure of this work capability. The model will be compared with different order logical combinations $k = \mathrm{3, 2}$, $k = \mathrm{3, 2}, 1$ and $k = \mathrm{3, 1}$. The performance of those logical combinations will be measured by Mean Absolute Error, Global Minimum Energy, Total Neuron Variation, Jaccard Similarity Index and Gower and Legendre Similarity Index. The results show that $k = \mathrm{3, 2}$ has the best overall performance due to its advantage of having the highest chances for the clauses to be satisfied and the absence of the first-order logic. Since it is also a non-systematic logical structure, it gains the highest diversity value during the learning phase

Análise de Performance de Técnicas de Optimização

Real-world complex optimization problems are one of the most complex challenges faced by scientific community. Achieving the best solution for a complex problem in an acceptable time interval is not always possible. In order to solve this problem, metaheuristics are one of the available resources. Having this in mind, finding a technique among others that presents better results in most executions would allow solution choosing to be more directive and assertive. Most used techniques comprise metaheuristics. These allow to find an acceptable solution in an acceptable time interval, even if the achieved solution was not the optimal possible. In this sense, this thesis intends to analyse four optimization techniques. Two population based techniques, one of them based in the behaviour of the bees in colony (Bee Colony) and another based in computational evolution (Genetic Algorithms). And, two single solution techniques, one based in memory structures (Tabu Search) and another based in the metallurgy industry (Simulated Annealing). These techniques were applied to two different optimization problems and computational results were registered and analysed. A prototype was built and used to obtain the results of applying metaheuristics to the Travelling Salesman problem (TSP) and the Knapsack Problem (KP). Evaluating the results, it was not possible to prove either that all algorithms are equivalent or that one of them is better in the majority of the cases.A resolução de problemas de otimização reais complexos constitui um dos grandes desafios científicos atuais. A possibilidade de obter as melhores soluções para os problemas nem sempre é possível em tempo útil e o recurso a técnicas de otimização para os resolver de forma eficaz e eficiente é constante. Neste sentido, encontrar uma técnica que sobressaia por entre as demais permitiria usar essas técnicas de forma mais direcionada e assertiva. Algumas das técnicas de otimização mais usadas são as meta-heurísticas. Estas permitem encontrar uma solução em tempo útil, mesmo não sendo a melhor solução possível. Neste contexto, a presente dissertação tem por vista a análise de quatro técnicas de otimização. Duas populacionais, sendo que uma técnica é baseada no comportamento dos enxames de abelhas (Bee Colony) e outra baseada na computação evolucionária, algoritmos genéticos (Genetic Algorithms). E, por posição, duas de solução única, a pesquisa tabu (Tabu Search), que se baseia nas estruturas de memória e uma técnica baseada na indústria metalúrgica, o arrefecimento simulado (Simulated Anealing). Estas técnicas foram aplicadas a dois problemas de otimização e os resultados computacionais, eficiência e eficácia das técnicas, foram registados e analisados. Um protótipo foi construído e utilizado para obter os resultados da aplicação das metaheurísticas ao problema de caixeiro viajante (TSP) e ao problema da mochila (KP). Após avaliação dos resultados, não foi possível provar que existia um algoritmo que se destacava entre os demais ou que os algoritmos eram equivalentes