An efficient particle swarm approach for mixed integer programming in reliability-redundancy optimization application

Reliability-redundancy is a recurrent problem in engineering where designed systems are meant to be very reliable. However, the cost of manufacturing very high reliability components increases exponentially, therefore redundancy of less reliable components is a palliative solution. Nonetheless, the question remains how many components of low reliability (and of what extent of reliability) should be coupled to produce a system of high reliability. In this paper, I try to reproduce the performance of particle swarm optimization (PSO) on solving a reliability redundancy-problem. Apart from the high variability, my best result showed to be better than the one presented in the paper

CFA optimizer: A new and powerful algorithm inspired by Franklin's and Coulomb's laws theory for solving the economic load dispatch problems

Copyright © 2018 John Wiley & Sons, Ltd. This paper presents a new efficient algorithm inspired by Franklin's and Coulomb's laws theory that is referred to as CFA algorithm, for finding the global solutions of optimal economic load dispatch problems in power systems. CFA is based on the impact of electrically charged particles on each other due to electrical attraction and repulsion forces. The effectiveness of the CFA in different terms is tested on basic benchmark problems. Then, the quality of the CFA to achieve accurate results in different aspects is examined and proven on economic load dispatch problems including 4 different size cases, 6, 10, 15, and 110-unit test systems. Finally, the results are compared with other inspired algorithms as well as results reported in the literature. The simulation results provide evidence for the well-organized and efficient performance of the CFA algorithm in solving great diversity of nonlinear optimization problems

A hybrid Jaya algorithm for reliability–redundancy allocation problems

© 2017 Informa UK Limited, trading as Taylor & Francis Group. This article proposes an efficient improved hybrid Jaya algorithm based on time-varying acceleration coefficients (TVACs) and the learning phase introduced in teaching–learning-based optimization (TLBO), named the LJaya-TVAC algorithm, for solving various types of nonlinear mixed-integer reliability–redundancy allocation problems (RRAPs) and standard real-parameter test functions. RRAPs include series, series–parallel, complex (bridge) and overspeed protection systems. The search power of the proposed LJaya-TVAC algorithm for finding the optimal solutions is first tested on the standard real-parameter unimodal and multi-modal functions with dimensions of 30–100, and then tested on various types of nonlinear mixed-integer RRAPs. The results are compared with the original Jaya algorithm and the best results reported in the recent literature. The optimal results obtained with the proposed LJaya-TVAC algorithm provide evidence for its better and acceptable optimization performance compared to the original Jaya algorithm and other reported optimal results

An approach for solving constrained reliability-redundancy allocation problems using cuckoo search algorithm

AbstractThe main goal of the present paper is to present a penalty based cuckoo search (CS) algorithm to get the optimal solution of reliability – redundancy allocation problems (RRAP) with nonlinear resource constraints. The reliability – redundancy allocation problem involves the selection of components' reliability in each subsystem and the corresponding redundancy levels that produce maximum benefits subject to the system's cost, weight, volume and reliability constraints. Numerical results of five benchmark problems are reported and compared. It has been shown that the solutions by the proposed approach are all superior to the best solutions obtained by the typical approaches in the literature are shown to be statistically significant by means of unpaired pooled t-test

Comparison of Simulated Annealing and Particle Swarm Optimization on Reliability-Redundancy Problem

Reliability-redundancy is a recurrent problem in engineering where designed systems are meant to be very reliable. However, the cost of manufacturing very high reliability components increases exponentially, therefore redundancy of less reliable components is a palliative solution. Nonetheless, the question remains how many components of low reliability (and of what extent of reliability) should be coupled to produce a system of high reliability. In this paper, I compare the performance of particle swarm optimization (PSO) and simulated annealing (SA) on a system of electricity distribution in a rural hospital. The results proved that PSO outperformed SA. In addition, considering the problem as reliability maximization and cost minimization bi-objective give a useful insight on how the cost increase exponentially at a certain given reliability of the system

Optimizing the selection of architecture for component-based system

Redundant components are commonly used for solving Redundancy Allocation Problems (RAP) and improving the reliability of complex systems. However, the use of such a strategy to minimize development costs while maintaining high quality attributes for building software architecture is a research challenge. The selection for an optimal architecture to meet this challenge is an inherently complex task due to the high volume of possible architectural candidates and the fundamental conflict between quality attributes. Current software evaluation methods focus on predicting the quality attributes and selecting Commercial-Off-the Shelf (COTS) components for COTS-Based applications rather than utilizing additional architectural evaluation methods that could increase the opportunity for obtaining a cost-effective solution for RAP. In this thesis, an architecture-based approach called Cost-Discount and Build-or-Buy for RAP (CD/BoB-RAP) is introduced to support the decision making for selecting the architecture with optimal components and level of redundancy that satisfies the technical and financial preferences. This approach consists of an optimization model that includes two architectural evaluation methods (CD-RAP and BoB-RAP) and applies three variants of Particle Swarm Optimization (PSO) algorithms. Statistical results showed a 74% reduction on the development cost using CD-RAP on an embedded system case study. Moreover, the application of a maximum possible improvement on the algorithms showed that Penalty Guided PSO (PG-PSO) had enhanced the quality of obtained solutions by 70% to 84% in comparison to other algorithms. The results of the CD-RAP and BoB-RAP were superior when compared to the results obtained from similar approaches. The overall results of this research have proven the potential benefits of the CD/BoB-RAP approach for software architecture evaluation, particularly, in selecting software architecture for minimizing the development cost maintaining a highly reliable system

State of the Art in the Optimisation of Wind Turbine Performance Using CFD

Wind energy has received increasing attention in recent years due to its sustainability and geographically wide availability. The efficiency of wind energy utilisation highly depends on the performance of wind turbines, which convert the kinetic energy in wind into electrical energy. In order to optimise wind turbine performance and reduce the cost of next-generation wind turbines, it is crucial to have a view of the state of the art in the key aspects on the performance optimisation of wind turbines using Computational Fluid Dynamics (CFD), which has attracted enormous interest in the development of next-generation wind turbines in recent years. This paper presents a comprehensive review of the state-of-the-art progress on optimisation of wind turbine performance using CFD, reviewing the objective functions to judge the performance of wind turbine, CFD approaches applied in the simulation of wind turbines and optimisation algorithms for wind turbine performance. This paper has been written for both researchers new to this research area by summarising underlying theory whilst presenting a comprehensive review on the up-to-date studies, and experts in the field of study by collecting a comprehensive list of related references where the details of computational methods that have been employed lately can be obtained

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms