A Novel Hybrid Firefly Algorithm for Global Optimization
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Application of the Firefly Algorithm for Optimal Production and Demand Forecasting at Selected Industrial Plant
Open Journal of Business and Management, 2020, 8, 2451-2459 https://www.scirp.org/journal/ojbm ISSN Online: 2329-3292 ISSN Print: 2329-3284 Application of the Firefly Algorithm for Optimal Production and Demand Forecasting at Selected Industrial Plant Abdulhadi Altherwi Industrial and Systems Engineering Department, Oakland University, Rochester, MI, USA How to cite this paper: Altherwi, A. (2020). Abstract Application of the Firefly Algorithm for Optimal Production and Demand Fore- Many production companies continue to face financial difficulties. The main casting at Selected Industrial Plant. Open purpose of this paper is to present an effective method for these companies to Journal of Business and Management, 8, maximize profits and minimize production costs. The problem presented 2451-2459. https://doi.org/10.4236/ojbm.2020.86151 here is initially developed as a linear programming model. To achieve the best results, the firefly algorithm (FA) is applied to solve the model and obtain an Received: October 15, 2020 optimum solution. In order to test the efficiency of the algorithm, Lindo Accepted: November 13, 2020 software is used, and the results of both algorithms are compared. A sensitiv- Published: November 16, 2020 ity analysis is applied to determine which products a company should pro- Copyright © 2020 by author(s) and duce in order to maximize profits. The result reveals that the FA, when com- Scientific Research Publishing Inc. pared with Lindo, can help achieve maximum profits by producing only one This work is licensed under the Creative product, thus minimizing production costs. For demand forecasting, the Commons Attribution International License (CC BY 4.0). -
Data Exploration in Evolutionary Reconstruction of PET Images Gray
Data exploration in evolutionary reconstruction of PET images ANGOR UNIVERSITY Gray, Cameron; Al-Maliki, Shatha; Vidal, Franck Genetic Programming and Evolvable Machines DOI: 10.1007/s10710-018-9330-7 PRIFYSGOL BANGOR / B Published: 01/09/2018 Peer reviewed version Cyswllt i'r cyhoeddiad / Link to publication Dyfyniad o'r fersiwn a gyhoeddwyd / Citation for published version (APA): Gray, C., Al-Maliki, S., & Vidal, F. (2018). Data exploration in evolutionary reconstruction of PET images. Genetic Programming and Evolvable Machines, 19(3), 391-419. https://doi.org/10.1007/s10710-018-9330-7 Hawliau Cyffredinol / General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. 24. Sep. 2021 Genetic Programming and Evolvable Machines manuscript No. (will be inserted by the editor) Data exploration in evolutionary reconstruction of PET images Cameron C. Gray · Shatha F. Al-Maliki · Franck P. Vidal the date of receipt and acceptance should be inserted later Abstract This work is based on a cooperative co-evolution algorithm called `Fly Algorithm', which is an evolutionary algorithm (EA) where individuals are called ‘flies’. -
Distributed Cognition As the Basis for Adaptation and Homeostasis In
DISTRIBUTEDCOGNITIONASTHEBASISFORADAPTATION ANDHOMEOSTASISINROBOTS james henry stovold Doctor of Philosophy University of York Computer Science July 2016 ABSTRACT Many researchers approach the problem of building autonomous systems by looking to biology for inspiration. This has given rise to a wide-range of artificial systems mimicking their biological counterparts—artificial neural networks, artificial endocrine systems, and artificial musculoskeletal systems are prime examples. While these systems are succinct and work well in isolation, they can become cumbersome and complicated when combined to perform more complex tasks. Autonomous behaviour is one such complex task. This thesis considers autonomy as the complex behaviour it is, and proposes a bottom-up approach to developing autonomous beha- viour from cognition. This consists of investigating how cognition can provide new approaches to the current limitations of swarm systems, and using this as the basis for one type of autonomous behaviour: artificial homeostasis. Distributed cognition, a form of emergent cognition, is most often described in terms of the immune system and social insects. By taking inspiration from distributed cognition, this thesis details the development of novel algorithms for cognitive decision-making and emergent identity in leaderless, homogenous swarms. Artificial homeostasis is provided to a robot through an architecture that combines the cognitive decision-making algorithm with a simple associative memory. This architecture is used to demonstrate how a simple architecture can endow a robot with the capacity to adapt to an unseen environment, and use that information to proactively seek out what it needs from the environment in order to maintain its internal state. iii CONTENTS Abstract iii Table of Contents v List of Figures viii List of Tables xi List of Code Listings xii Acknowledgements xiii Declaration xv 1 introduction 1 1.1 Motivation . -
An Improved Bees Algorithm Local Search Mechanism for Numerical Dataset
An Improved Bees Algorithm Local Search Mechanism for Numerical dataset Prepared By: Aras Ghazi Mohammed Al-dawoodi Supervisor: Dr. Massudi bin Mahmuddin DISSERTATION SUBMITTED TO THE AWANG HAD SALLEH GRADUATE SCHOOL OF COMPUTING UUM COLLEGE OF ARTS AND SCIENCES UNIVERSITI UTARA MALAYSIA IN PARITIAL FULFILLMENT OF THE REQUIREMENT FOR THE DEGREE OF MASTER IN (INFORMATION TECHNOLOGY) 2014 - 2015 Permission to Use In presenting this dissertation report in partial fulfilment of the requirements for a postgraduate degree from University Utara Malaysia, I agree that the University Library may make it freely available for inspection. I further agree that permission for the copying of this report in any manner, in whole or in part, for scholarly purpose may be granted by my supervisor(s) or, in their absence, by the Dean of Awang Had Salleh Graduate School of Arts and Sciences. It is understood that any copying or publication or use of this report or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to University Utara Malaysia for any scholarly use which may be made of any material from my report. Requests for permission to copy or to make other use of materials in this project report, in whole or in part, should be addressed to: Dean of Awang Had Salleh Graduate School of Arts and Sciences UUM College of Arts and Sciences University Utara Malaysia 06010 UUM Sintok i Abstrak Bees Algorithm (BA), satu prosedur pengoptimuman heuristik, merupakan salah satu teknik carian asas yang berdasarkan kepada aktiviti pencarian makanan lebah. -
Application of the Firefly Algorithm for Solving the Economic Emissions
Hindawi Publishing Corporation International Journal of Combinatorics Volume 2011, Article ID 523806, 23 pages doi:10.1155/2011/523806 Research Article Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem Theofanis Apostolopoulos and Aristidis Vlachos Department of Informatics, University of Piraeus, 80 Karaoli and Dimitriou Street, 18534 Piraeus, Greece Correspondence should be addressed to Aristidis Vlachos, [email protected] Received 30 August 2010; Accepted 14 November 2010 Academic Editor: Hajo Broersma Copyright q 2011 T. Apostolopoulos and A. Vlachos. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Efficient and reliable power production is necessary to meet both the profitability of power systems operations and the electricity demand, taking also into account the environmental concerns about the emissions produced by fossil-fuelled power plants. The economic emission load dispatch problem has been defined and applied in order to deal with the optimization of these two conflicting objectives, that is, the minimization of both fuel cost and emission of generating units. This paper introduces and describes a solution to this famous problem using a new metaheuristic nature-inspired algorithm, called firefly algorithm, which was developed by Dr. Xin-She Yang at Cambridge University in 2007. A general formulation of this algorithm is presented together with an analytical mathematical modeling to solve this problem by a single equivalent objective function. The results are compared with those obtained by alternative techniques proposed by the literature in order to show that it is capable of yielding good optimal solutions with proper selection of control parameters. -
Firefly Algorithm Applied to Integer Programming Problems
Recent Advances in Mathematics Firefly Algorithm Applied to Integer Programming Problems Nebojsa BACANIN, Ivona BRAJEVIC, Milan TUBA Faculty of Computer Science Megatrend University Belgrade Bulevar umetnosti 29, N. Belgrade SERBIA [email protected], [email protected], [email protected], Abstract: - Firefly algorithm is a recently added member of the swarm intelligence heuristics family. In this paper the firefly algorithm is adjusted and applied to integer programming problems. In order to deal with integer programming problems, firefly algorithm rounds the parameter values to the closest integer after producing new solutions. The performance of firefly algorithm is tested on seven problems widely used in the literature. Artificial bee colony algorithm is also implemented for comparison with the results of the firefly algorithm. Experimental results show that the firefly algorithm proved to be superior in almost all tested problems. Key-Words: - Firefly algorithm, Optimization metaheuristics, Integer programming, Swarm intelligence 1 Introduction Delaunay triangulation of the free configuration Linear programming optimization can find optimal space [3] are also solvable by integer programming solution to problems with hundreds of thousands of method. The entire class of problems referred to as constraints and variables. Modeling strength and sequencing, scheduling and routing is inherently solvability makes linear programming technique integer programs. This class of problems might for applicable in real life problems. Integer example deal with the scheduling of students and programming is a form of mathematical classrooms in such a way that the number of optimization program where some or all employed students who cannot take their first choice of classes variables are required to have integer values. -
Comparison of Hybrid Firefly Algorithms for Power Allocation in a TV White Space Network
International Journal of Computer Applications (0975 – 8887) Volume 178 – No. 38, August 2019 Comparison of Hybrid Firefly Algorithms for Power Allocation in a TV White Space Network Kennedy K. Ronoh George Kamucha Tonny K. Omwansa School of Computing and Department of Electrical and School of Computing and Informatics Information Engineering Informatics University of Nairobi University of Nairobi University of Nairobi Nairobi, Kenya Nairobi, Kenya Nairobi, Kenya ABSTRACT 1GHz spectrum. TV white spaces (TVWS) can be used by Secondary Users Regulatory authorities worldwide have mandated the use of (SUs) through Dynamic Spectrum Access (DSA) as long as geo-location database (GLDB) for protection of incumbent they do not cause harmful interference to Primary Users users in TVWS network. GLDB is used by a SU or White (PUs). Due spectrum scarcity, there is increasing demand for Space Device (WSD) to find the set of frequency channels DSA. When there is a high density of SUs in a TVWS that can be used on a secondary basis at a given area and at network such as cellular access to TVWS, problem of any given time [5], [6], [7]. GLDB is populated through the interference among SUs will arise. Possibility of harmful use of a propagation model. The database contains estimated interference to PUs may also arise. Optimization of power power levels of PUs for any point in a particular region of allocation is therefore necessary to reduce the level of interest. The WSD, which has a Cognitive Radio System interference among SUs and to protect PUs against harmful (CRS), queries a central database. -
Synchronization of Fireflies Can Improve Or Inspire Robotic Swarm
elligenc International Journal of Swarm nt e I an rm d a E w v S o f l u o t l i o a n n a r u r Intelligence and Evolutionary y o C J l o a m n p ISSN:o 2090-4908 i t u a t a n t r i o e t n I n Computation Editorial Synchronization of Fireflies can Improve or Inspire Robotic Swarm Seelu Palai* Department of Computer Science, Parul University, Limda, Waghodia, Gujarat DESCRIPTION issue within the scientific community, many researchers believe Despite their simple brains, synchronized fireflies have that it helps fireflies for locating mates, protecting themselves something to show us about AI. A recent discovery on the from their predators and attracting their potential prey. within insect's amazing light shows could help researchers acquire fresh the firefly algorithm, the target function of a given optimization insight into swarm robotics. It seems, the synchronized fireflies' problem is related to this flashing light or candlepower which light displays are even more subtle than scientists realized, and helps the swarm of fireflies to maneuver to brighter and more it's to try to with the insect's three-dimensional positioning. They attractive locations so as to get efficient optimal solutions. found that fireflies behave differently after they are alone versus Multiple conflicting objectives arise naturally in most real-world in groups. In seems, synchronized fireflies what their neighbors combinatorial optimization problems. Several principles and do, rather than blinking consistent with any inherent rhythm. methods are developed and proposed for over a decade so as to Then they modify their blinking pattern to match those around resolve problems. -
Threshold Selection, Mitosis and Dual Mutation in Cooperative Co-Evolution
Threshold selection, mitosis and dual mutation in cooperative co-evolution: application to medical 3d tomography Franck Vidal, Evelyne Lutton, Jean Louchet, Jean-Marie Rocchisani To cite this version: Franck Vidal, Evelyne Lutton, Jean Louchet, Jean-Marie Rocchisani. Threshold selection, mitosis and dual mutation in cooperative co-evolution: application to medical 3d tomography. PPSN 2010, 11th International Conference on Parallel Problem Solving From Nature., Sep 2010, Krakow, Poland. hal-00783841 HAL Id: hal-00783841 https://hal.inria.fr/hal-00783841 Submitted on 1 Feb 2013 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Threshold selection, mitosis and dual mutation in cooperative co-evolution: application to medical 3D tomography Franck P. Vidal1, Evelyne Lutton2, Jean Louchet3, and Jean-Marie Rocchisani4 1 Department of Radiation Oncology, University of California, San Diego, CA. [email protected] 2 INRIA - Saclay-ˆIle-de-France, AVIZ team, Orsay, France. [email protected] 3 Artenia, Chˆatillon, France. [email protected] 4 Paris XIII University, UFR SMBH & Avicenne hospital, Bobigny, France. [email protected] Abstract. We present and analyse the behaviour of specialised opera- tors designed for cooperative coevolution strategy in the framework of 3D tomographic PET reconstruction. -
Three Novel Quantum-Inspired Swarm Optimization Algorithms Using
www.nature.com/scientificreports OPEN Three novel quantum‑inspired swarm optimization algorithms using diferent bounded potential felds Manuel S. Alvarez‑Alvarado1*, Francisco E. Alban‑Chacón2, Erick A. Lamilla‑Rubio2, Carlos D. Rodríguez‑Gallegos3 & Washington Velásquez1 Based on the behavior of the quantum particles, it is possible to formulate mathematical expressions to develop metaheuristic search optimization algorithms. This paper presents three novel quantum‑ inspired algorithms, which scenario is a particle swarm that is excited by a Lorentz, Rosen–Morse, and Coulomb‑like square root potential felds, respectively. To show the computational efcacy of the proposed optimization techniques, the paper presents a comparative study with the classical particle swarm optimization (PSO), genetic algorithm (GA), and frefy algorithm (FFA). The algorithms are used to solve 24 benchmark functions that are categorized by unimodal, multimodal, and fxed‑ dimension multimodal. As a fnding, the algorithm inspired in the Lorentz potential feld presents the most balanced computational performance in terms of exploitation (accuracy and precision), exploration (convergence speed and acceleration), and simulation time compared to the algorithms previously mentioned. A deeper analysis reveals that a strong potential feld inside a well with weak asymptotic behavior leads to better exploitation and exploration attributes for unimodal, multimodal, and fxed‑multimodal functions. Gradient-based and Hessian-based algorithms are widely use in the literature to solve optimization problems in engineering. Tis is due to their computational efciency, which typically require little problem-specifc parameter tuning1. Te Gradient-based and Hessian-based algorithms employ iterative process that involves multivariate scalar function that packages all the information of the partial derivatives of the objective function (i.e., Gradient function or Hessian matrix) to reach the solution. -
Applying Firefly Algorithm to Solve the Problem of Balancing Curricula
(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 1, 2019 Applying FireFly Algorithm to Solve the Problem of Balancing Curricula 1* 2* 3 4 5 6 Jose´ Miguel Rubio , Cristian L. Vidal-Silva , Ricardo Soto , Erika Madariaga , Franklin Johnson , Luis Carter 1 ´Area Acad´emica de Inform´atica y Telecomunicaciones, Universidad Tecnol´ogica de Chile INACAP, Santiago, Chile 2Ingenier´ıa Civil Inform´atica, Escuela de Ingenier´ıa, Universidad Vi˜na del Mar, Vi˜na del Mar, Chile 3Escuela de Ingenier´ıa Inform´atica, Facultad de Ingenier´ıa, Pontificia Universidad Cat´olica de Valpara´ıso, Valpara´ıso, Chile 4Ingenier´ıa Inform´atica, Facultad de Ingenier´ıa, Ciencia y Tecnolog´ıa, Universidad Bernardo O’Higgins, Santiago, Chile 5Depto. Disciplinario de Computaci´on e Inform´atica, Facultad de Ingenier´ıa, Universidad de Playa Ancha, Valpara´so, Chile 6Ingenier´ıa Civil Industrial, Facultad de Ingenier´ıa, Universidad Aut´onoma de Chile, Talca, Chile Abstract—The problem of assigning a balanced academic the paradigm of programming with restrictions and hybrid curriculum to academic periods of a curriculum, that is, the algorithms using genetic algorithms, collaboration schemes, balancing curricula, represents a traditional challenge for every and local searches, among other techniques. educational institution which look for a match among students and professors. This article proposes a solution for the balancing The present work focuses on solving the test instances curricula problem using an optimization technique based on the recognized by the CSPLib [1], and a few real situations of attraction of fireflies (FA) meta-heuristic. We perform a set of test the problem in the study programs of the computer area of and real instances to measure the performance of our solution two Chilean universities. -
Artificial Evolution Strategy for PET Reconstruction
1 Artificial Evolution Strategy for PET Reconstruction Franck P. Vidal, Yoann L. Pavia, Jean-Marie Rocchisani, Jean Louchet, and Evelyne´ Lutton Abstract—This paper shows new resutls of our artificial PET evolution algorithm for positron emission tomography (PET) reconstruction. This imaging technique produces datasets cor- CT responding to the concentration of positron emitters within the PET CT PET/CT patient. Fully three-dimensional (3D) tomographic reconstruction requires high computing power and leads to many challenges. Our aim is to produce high quality datasets in a time that is clin- PET/CT ically acceptable. Our method is based on a co-evolution strategy called the “Fly algorithm”. Each fly represents a point in space PET CT PET/CT and mimics a positron emitter. Each fly position is progressively (a) PET-CT scan of a patient with lung (b) Schema of a PET acquisition optimised using evolutionary computing to closely match the data cancer and showing hilar lymph nodes. process. measured by the imaging system. The performance of each fly is assessed based on its positive or negative contribution to the Fig. 1. PET imaging. performance of the whole population. The final population of flies approximates the radioactivity concentration. This approach has shown promising results on numerical phantom models. The size of objects and their relative concentrations can be calculated in two-dimensional (2D) space. In 3D, complex shapes can be reconstructed. In this paper, we demonstrate the ability of the algorithm to fidely reconstruct more anatomically realistic volumes. Index Terms—Evolutionary computation, inverse problems, adaptive algorithm, Nuclear medicine, Positron emission tomog- Fig.