4 research outputs found
A Literature Review of Cuckoo Search Algorithm
Optimization techniques play key role in real world problems. In many situations where decisions are taken based on random search they are used. But choosing optimal Optimization algorithm is a major challenge to the user. This paper presents a review on Cuckoo Search Algorithm which can replace many traditionally used techniques. Cuckoo search uses Levi flight strategy based on Egg laying Radius in deriving the solution specific to problem. CS optimization algorithm increases the efficiency, accuracy, and convergence rate. Different categories of the cuckoo search and several applications of the cuckoo search are reviewed. Keywords: Cuckoo Search Optimization, Applications , Levy Flight DOI: 10.7176/JEP/11-8-01 Publication date:March 31st 202
Nature-inspired algorithms for solving some hard numerical problems
Optimisation is a branch of mathematics that was developed to find the optimal solutions,
among all the possible ones, for a given problem. Applications of optimisation techniques
are currently employed in engineering, computing, and industrial problems. Therefore, optimisation is a very active research area, leading to the publication of a large number of
methods to solve specific problems to its optimality.
This dissertation focuses on the adaptation of two nature inspired algorithms that, based
on optimisation techniques, are able to compute approximations for zeros of polynomials
and roots of non-linear equations and systems of non-linear equations.
Although many iterative methods for finding all the roots of a given function already
exist, they usually require: (a) repeated deflations, that can lead to very inaccurate results
due to the problem of accumulating rounding errors, (b) good initial approximations to the
roots for the algorithm converge, or (c) the computation of first or second order derivatives,
which besides being computationally intensive, it is not always possible.
The drawbacks previously mentioned served as motivation for the use of Particle Swarm
Optimisation (PSO) and Artificial Neural Networks (ANNs) for root-finding, since they are
known, respectively, for their ability to explore high-dimensional spaces (not requiring good
initial approximations) and for their capability to model complex problems. Besides that,
both methods do not need repeated deflations, nor derivative information.
The algorithms were described throughout this document and tested using a test suite of
hard numerical problems in science and engineering. Results, in turn, were compared with
several results available on the literature and with the well-known Durand–Kerner method,
depicting that both algorithms are effective to solve the numerical problems considered.A Optimização Ă© um ramo da matemática desenvolvido para encontrar as soluções Ăłptimas, de entre todas as possĂveis, para um determinado problema. Actualmente, sĂŁo várias as
técnicas de optimização aplicadas a problemas de engenharia, de informática e da indústria.
Dada a grande panóplia de aplicações, existem inúmeros trabalhos publicados que propõem
mĂ©todos para resolver, de forma Ăłptima, problemas especĂficos.
Esta dissertação foca-se na adaptação de dois algoritmos inspirados na natureza que,
tendo como base técnicas de optimização, são capazes de calcular aproximações para zeros
de polinĂłmios e raĂzes de equações nĂŁo lineares e sistemas de equações nĂŁo lineares.
Embora já existam muitos mĂ©todos iterativos para encontrar todas as raĂzes ou zeros de
uma função, eles usualmente exigem: (a) deflações repetidas, que podem levar a resultados
muito inexactos, devido ao problema da acumulação de erros de arredondamento a cada
iteração; (b) boas aproximações iniciais para as raĂzes para o algoritmo convergir, ou (c) o
cálculo de derivadas de primeira ou de segunda ordem que, além de ser computacionalmente
intensivo, para muitas funções Ă© impossĂvel de se calcular.
Estas desvantagens motivaram o uso da Optimização por Enxame de PartĂculas (PSO) e
de Redes Neurais Artificiais (RNAs) para o cálculo de raĂzes. Estas tĂ©cnicas sĂŁo conhecidas,
respectivamente, pela sua capacidade de explorar espaços de dimensão superior (não exigindo
boas aproximações iniciais) e pela sua capacidade de modelar problemas complexos. Além
disto, tais técnicas não necessitam de deflações repetidas, nem do cálculo de derivadas.
Ao longo deste documento, os algoritmos sĂŁo descritos e testados, usando um conjunto de
problemas numĂ©ricos com aplicações nas ciĂŞncias e na engenharia. Os resultados foram comparados com outros disponĂveis na literatura e com o mĂ©todo de Durand–Kerner, e sugerem
que ambos os algoritmos são capazes de resolver os problemas numéricos considerados
Optimization of systems reliability by metaheuristic approach
The application of metaheuristic approaches in addressing the reliability of systems through optimization is of greater interest to researchers and designers in recent years. Reliability optimization has become an essential part of the design and operation of largescale manufacturing systems. This thesis addresses the optimization of system-reliability for series–parallel systems to solve redundant, continuous, and combinatorial optimization problems in reliability engineering by using metaheuristic approaches (MAs). The problem is to select the best redundancy strategy, component, and redundancy level for each subsystem to maximize the system reliability under system-level constraints. This type of problem involves the selection of components with multiple choices and redundancy levels that yield the maximum benefits, and it is subject to the cost and weight constraints at the system level. These are very common and realistic problems faced in the conceptual design of numerous engineering systems. The development of efficient solutions to these problems is becoming progressively important because mechanical systems are becoming increasingly complex, while development plans are decreasing in size and reliability requirements are rapidly changing and becoming increasingly difficult to adhere to. An optimal design solution can be obtained very frequently and more quickly by using genetic algorithm redundancy allocation problems (GARAPs). In general, redundancy allocation problems (RAPs) are difficult to solve for real cases, especially in large-scale situations. In this study, the reliability optimization of a series–parallel by using a genetic algorithm (GA) and statistical analysis is considered. The approach discussed herein can be applied to address the challenges in system reliability that includes redundant numbers of carefully chosen modules, overall cost, and overall weight.
Most related studies have focused only on the single-objective optimization of RAP. Multiobjective optimization has not yet attracted much attention. This research project examines the multiobjective situation by focusing on multiobjective formulation, which is useful in maximizing system reliability while simultaneously minimizing system cost and weight to solve the RAP. The present study applies a methodology for optimizing the reliability of a series–parallel system based on multiobjective optimization and multistate reliability by using a hybrid GA and a fuzzy function. The study aims to determine the strategy for selecting the degree of redundancy for every subsystem to exploit the general system reliability depending on the overall cost and weight limitations. In addition, the outcomes of the case study for optimizing the reliability of the series–parallel system are presented, and the relationships with previously investigated phenomena are presented to determine the performance of the GA under review. Furthermore, this study established a new metaheuristic-based technique for resolving multiobjective optimization challenges, such as the common reliability redundancy allocation problem. Additionally, a new simulation process was developed to generate practical tools for designing reliable series–parallel systems. Hence, metaheuristic methods were applied for solving such difficult and complex problems. In addition, metaheuristics provide a useful compromise between the amount of computation time required and the quality of the approximated solution space. The industrial challenges include the maximization of system reliability subject to limited system cost and weight, minimization of system weight subject to limited system cost and the system reliability requirements and increasing of quality components through optimization and system reliability. Furthermore, a real-life situation research on security control of a gas turbine in the overspeed state was explored in this study with the aim of verifying the proposed algorithm from the context of system optimization