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Journal articles on the topic 'Evolutionary Optimiser'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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1

Ab. Rashid, M. F. F., N. M. Z. Nik Mohamed, and A. N. Mohd Rose. "A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing." Journal of Mechanical Engineering and Sciences 13, no. 4 (2019): 5905–21. http://dx.doi.org/10.15282/jmes.13.4.2019.13.0469.

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Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) are traditionally optimised independently. However recently, integrated ASP and ALB optimisation has become more relevant to obtain better quality solution and to reduce time to market. Despite many optimisation algorithms that were proposed to optimise this problem, the existing researches on this problem were limited to Evolutionary Algorithm (EA), Ant Colony Optimisation (ACO), and Particle Swarm Optimisation (PSO). This paper proposed a modified Artificial Bee Colony algorithm (MABC) to optimise the integrated ASP and ALB p

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al-Rifaie, Mohammad Majid. "Exploration and Exploitation Zones in a Minimalist Swarm Optimiser." Entropy 23, no. 8 (2021): 977. http://dx.doi.org/10.3390/e23080977.

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The trade off between exploration and exploitation is one of the key challenges in evolutionary and swarm optimisers which are led by guided and stochastic search. This work investigates the exploration and exploitation balance in a minimalist swarm optimiser in order to offer insights into the population’s behaviour. The minimalist and vector-stripped nature of the algorithm—dispersive flies optimisation or DFO—reduces the challenges of understanding particles’ oscillation around constantly changing centres, their influence on one another, and their trajectory. The aim is to examine the popul

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Kunakote, Tawatchai, and Sujin Bureerat. "Surrogate-Assisted Multiobjective Evolutionary Algorithms for Structural Shape and Sizing Optimisation." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/695172.

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The work in this paper proposes the hybridisation of the well-established strength Pareto evolutionary algorithm (SPEA2) and some commonly used surrogate models. The surrogate models are introduced to an evolutionary optimisation process to enhance the performance of the optimiser when solving design problems with expensive function evaluation. Several surrogate models including quadratic function, radial basis function, neural network, and Kriging models are employed in combination with SPEA2 using real codes. The various hybrid optimisation strategies are implemented on eight simultaneous sh

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Giel, Oliver, and Per Kristian Lehre. "On the Effect of Populations in Evolutionary Multi-Objective Optimisation." Evolutionary Computation 18, no. 3 (2010): 335–56. http://dx.doi.org/10.1162/evco_a_00013.

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Multi-objective evolutionary algorithms (MOEAs) have become increasingly popular as multi-objective problem solving techniques. An important open problem is to understand the role of populations in MOEAs. We present two simple bi-objective problems which emphasise when populations are needed. Rigorous runtime analysis points out an exponential runtime gap between the population-based algorithm simple evolutionary multi-objective optimiser (SEMO) and several single individual-based algorithms on this problem. This means that among the algorithms considered, only the population-based MOEA is suc

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Delelegn, S. W., A. Pathirana, B. Gersonius, A. G. Adeogun, and K. Vairavamoorthy. "Multi-objective optimisation of cost–benefit of urban flood management using a 1D2D coupled model." Water Science and Technology 63, no. 5 (2011): 1053–59. http://dx.doi.org/10.2166/wst.2011.290.

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This paper presents a multi-objective optimisation (MOO) tool for urban drainage management that is based on a 1D2D coupled model of SWMM5 (1D sub-surface flow model) and BreZo (2D surface flow model). This coupled model is linked with NSGA-II, which is an Evolutionary Algorithm-based optimiser. Previously the combination of a surface/sub-surface flow model and evolutionary optimisation has been considered to be infeasible due to the computational demands. The 1D2D coupled model used here shows a computational efficiency that is acceptable for optimisation. This technological advance is the re

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Sartakhti, Moein Salimi, Ahmad Yoosofan, Ali Asghar Fatehi, and Ali Rahimi. "Single Document Summarization Based on Grey Wolf Optimization." Global Journal of Computer Sciences: Theory and Research 10, no. 2 (2020): 48–56. http://dx.doi.org/10.18844/gjcs.v10i2.5807.

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The amazing growth of online services has caused an information explosion issue. Text summarisation is condensing the text into a small version and preserving its overall concept. Text summarisation is an important way to extract significant information from documents and offer that information to the user in an abbreviated form while preserving its major content. For human beings, it is very difficult to summarise large documents. To do this, this paper uses some sentence features and word features. These features assign scores to all the sentences. In this paper, we combine these features by

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Marrero, Alejandro, Eduardo Segredo, Coromoto León, and Carlos Segura. "A Memetic Decomposition-Based Multi-Objective Evolutionary Algorithm Applied to a Constrained Menu Planning Problem." Mathematics 8, no. 11 (2020): 1960. http://dx.doi.org/10.3390/math8111960.

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Encouraging healthy and balanced diet plans is one of the most important action points for governments around the world. Generating healthy, balanced and inexpensive menu plans that fulfil all the recommendations given by nutritionists is a complex and time-consuming task; because of this, computer science has an important role in this area. This paper deals with a novel constrained multi-objective formulation of the menu planning problem specially designed for school canteens that considers the minimisation of the cost and the minimisation of the level of repetition of the specific courses an

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8

Ashrafian, Ali, Naser Safaeian Hamzehkolaei, Ngakan Ketut Acwin Dwijendra, and Maziar Yazdani. "An Evolutionary Neuro-Fuzzy-Based Approach to Estimate the Compressive Strength of Eco-Friendly Concrete Containing Recycled Construction Wastes." Buildings 12, no. 8 (2022): 1280. http://dx.doi.org/10.3390/buildings12081280.

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There has been a significant increase in construction and demolition (C&D) waste due to the growth of cities and the need for new construction, raising concerns about the impact on the environment of these wastes. By utilising recycled C&D waste, especially in concretes used in construction, further environmental damage can be prevented. By using these concretes, energy consumption and environmental impacts of concrete production can be reduced. The behaviour of these types of concrete in laboratories has been extensively studied, but reliable methods for estimating their behaviour bas

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Gonzalez, L. F., D. S. Lee, K. Srinivas, and K. C. Wong. "Single and multi–objective UAV aerofoil optimisation via hierarchical asynchronous parallel evolutionary algorithm." Aeronautical Journal 110, no. 1112 (2006): 659–72. http://dx.doi.org/10.1017/s0001924000001524.

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Abstract Unmanned aerial vehicle (UAV) design tends to focus on sensors, payload and navigation systems, as these are the most expensive components. One area that is often overlooked in UAV design is airframe and aerodynamic shape optimisation. As for manned aircraft, optimisation is important in order to extend the operational envelope and efficiency of these vehicles. A traditional approach to optimisation is to use gradient-based techniques. These techniques are effective when applied to specific problems and within a specified range. These methods are efficient for finding optimal global s

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Saravanan, R., S. Ramabalan, and C. Balamurugan. "Multiobjective trajectory planner for industrial robots with payload constraints." Robotica 26, no. 6 (2008): 753–65. http://dx.doi.org/10.1017/s0263574708004359.

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SUMMARYA general new methodology using evolutionary algorithms viz., Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Differential Evolution (MODE), for obtaining optimal trajectory planning of an industrial robot manipulator (PUMA 560 robot) in the presence of fixed and moving obstacles with payload constraint is presented. The problem has a multi-criterion character in which six objective functions, 32 constraints and 288 variables are considered. A cubic NURBS curve is used to define the trajectory. The average fuzzy membership function method is used to select

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Barham, Reham Shawqi, Ahmad Sharieh, and Azzam Sleit. "A meta-heuristic framework based on clustering and preprocessed datasets for solving the link prediction problem." Journal of Information Science 45, no. 6 (2018): 794–817. http://dx.doi.org/10.1177/0165551518816296.

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This study presents a solution to a problem commonly known as link prediction problem. Link prediction problem interests in predicting the possibility of appearing a connection between two nodes of a network, while there is no connection between these nodes in the present state of the network. Finding a solution to link prediction problem attracts variety of computer science fields such as data mining and machine learning. This attraction is due to its importance for many applications such as social networks, bioinformatics and co-authorship networks. Towards solving this problem, Evolutionary

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Sepulveda Palacios, Eduardo, and Howard Smith. "Impact of mission requirements on the design of low observable UCAV configurations." Aircraft Engineering and Aerospace Technology 91, no. 10 (2019): 1295–307. http://dx.doi.org/10.1108/aeat-09-2018-0249.

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Purpose The purpose of this paper is to characterise the effects of mission and performance parameters on the design space of low observable subsonic unmanned combat aerial vehicles (UCAVs) operating in typical Hi-Lo-Hi ground strike missions. Design/methodology/approach Conceptual design methodologies appropriate to low observable, tailless UCAVs have been integrated into a multidisciplinary aircraft design environment, GENUS, developed at Cranfield University’s aircraft design group. A basic Hi-Lo-Hi mission is designed and a baseline configuration is established through the GENUS framework.

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Neshat, Mehdi, Nataliia Sergiienko, Seyedali Mirjalili, Meysam Majidi Nezhad, Giuseppe Piras, and Davide Astiaso Garcia. "Multi-Mode Wave Energy Converter Design Optimisation Using an Improved Moth Flame Optimisation Algorithm." Energies 14, no. 13 (2021): 3737. http://dx.doi.org/10.3390/en14133737.

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Ocean renewable wave power is one of the more encouraging inexhaustible energy sources, with the potential to be exploited for nearly 337 GW worldwide. However, compared with other sources of renewables, wave energy technologies have not been fully developed, and the produced energy price is not as competitive as that of wind or solar renewable technologies. In order to commercialise ocean wave technologies, a wide range of optimisation methodologies have been proposed in the last decade. However, evaluations and comparisons of the performance of state-of-the-art bio-inspired optimisation algo

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14

Bhat, Shantanu S., Jisheng Zhao, John Sheridan, Kerry Hourigan, and Mark C. Thompson. "Evolutionary shape optimisation enhances the lift coefficient of rotating wing geometries." Journal of Fluid Mechanics 868 (April 11, 2019): 369–84. http://dx.doi.org/10.1017/jfm.2019.183.

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Wing shape is an important factor affecting the aerodynamic performance of wings of monocopters and flapping-wing micro air vehicles. Here, an evolutionary structural optimisation method is adapted to optimise wing shape to enhance the lift force due to aerodynamic pressure on the wing surfaces. The pressure distribution is observed to vary with the span-based Reynolds number over a range covering most insects and samaras. Accordingly, the optimised wing shapes derived using this evolutionary approach are shown to adjust with Reynolds number. Moreover, these optimised shapes exhibit significan

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15

Williams, Jonathan. "Multiple timescales of evolution." Behavioral and Brain Sciences 29, no. 4 (2006): 426–27. http://dx.doi.org/10.1017/s0140525x06439091.

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Keller & Miller's (K&M's) treatment of disorders usefully avoids diagnostic minutiae; but it needs more real-world constraints. Classifying processes by their evolutionary age helps to clarify both evolution and current function. Evolutionarily old, optimised, normative processes deserve special recognition, because they can be studied in animals and computers, and because they provide the machinery through which disorder-related polymorphisms act.

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16

McGerty, Sean, and Frank Moisiadis. "Optimised Random Mutations for Evolutionary Algorithms." International Journal of Artificial Intelligence & Applications 5, no. 4 (2014): 15–34. http://dx.doi.org/10.5121/ijaia.2014.5402.

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Horvath, Dragos, J. Brown, Gilles Marcou, and Alexandre Varnek. "An Evolutionary Optimizer of libsvm Models." Challenges 5, no. 2 (2014): 450–72. http://dx.doi.org/10.3390/challe5020450.

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18

Yamamoto, Toshihiko, Hiroshi Sato, and Akira Namatame. "Evolutionary optimised consensus and synchronisation networks." International Journal of Bio-Inspired Computation 3, no. 3 (2011): 187. http://dx.doi.org/10.1504/ijbic.2011.040317.

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19

Komatsu, Takanori, and Akira Namatame. "Dynamic diffusion in evolutionary optimised networks." International Journal of Bio-Inspired Computation 3, no. 6 (2011): 384. http://dx.doi.org/10.1504/ijbic.2011.043608.

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20

Ohenoja, Markku, Aki Sorsa, and Kauko Leiviskä. "Model Structure Optimization for Fuel Cell Polarization Curves." Computers 7, no. 4 (2018): 60. http://dx.doi.org/10.3390/computers7040060.

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The applications of evolutionary optimizers such as genetic algorithms, differential evolution, and various swarm optimizers to the parameter estimation of the fuel cell polarization curve models have increased. This study takes a novel approach on utilizing evolutionary optimization in fuel cell modeling. Model structure identification is performed with genetic algorithms in order to determine an optimized representation of a polarization curve model with linear model parameters. The optimization is repeated with a different set of input variables and varying model complexity. The resulted mo

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21

Malaranbal, C., and G. Sumalatha. "Evolutionary Energy Hole Alleviation by handling Inconsistency in Cluster Head Selection for Optimized Routing in WSN." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 549–58. http://dx.doi.org/10.5373/jardcs/v11sp10/20192841.

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22

Wong, Yuwa. "Optimism, Pessimism, and Evolutionary Thinking." Politics and the Life Sciences 13, no. 1 (1994): 35–37. http://dx.doi.org/10.1017/s073093840002219x.

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23

Abdel-Basset, Mohamed, Reda Mohamed, Karam M. Sallam, and Ripon K. Chakrabortty. "Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm." Mathematics 10, no. 19 (2022): 3466. http://dx.doi.org/10.3390/math10193466.

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This paper introduces a novel physical-inspired metaheuristic algorithm called “Light Spectrum Optimizer (LSO)” for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is

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Moen, H. J. F., T. Sparr, and S. Kristoffersen. "Improved radar detection using evolutionary optimised filter." IET Radar, Sonar & Navigation 6, no. 9 (2012): 803–12. http://dx.doi.org/10.1049/iet-rsn.2012.0099.

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25

van Bommel, Patrick. "Experiences with EDO: An evolutionary database optimizer." Data & Knowledge Engineering 13, no. 3 (1994): 243–63. http://dx.doi.org/10.1016/0169-023x(94)00017-4.

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Bandyopadhyay, Sanghamitra, and Ujjwal Maulik. "An Improved Evolutionary Algorithm as Function Optimizer." IETE Journal of Research 46, no. 1-2 (2000): 47–56. http://dx.doi.org/10.1080/03772063.2000.11416134.

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27

Saremi, Shahrzad, Seyedeh Zahra Mirjalili, and Seyed Mohammad Mirjalili. "Evolutionary population dynamics and grey wolf optimizer." Neural Computing and Applications 26, no. 5 (2014): 1257–63. http://dx.doi.org/10.1007/s00521-014-1806-7.

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Pošík, Petr, Waltraud Huyer, and László Pál. "A Comparison of Global Search Algorithms for Continuous Black Box Optimization." Evolutionary Computation 20, no. 4 (2012): 509–41. http://dx.doi.org/10.1162/evco_a_00084.

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Four methods for global numerical black box optimization with origins in the mathematical programming community are described and experimentally compared with the state of the art evolutionary method, BIPOP-CMA-ES. The methods chosen for the comparison exhibit various features that are potentially interesting for the evolutionary computation community: systematic sampling of the search space (DIRECT, MCS) possibly combined with a local search method (MCS), or a multi-start approach (NEWUOA, GLOBAL) possibly equipped with a careful selection of points to run a local optimizer from (GLOBAL). The

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Moravec, Jerry. "Hand contour classification using evolutionary algorithm." Information Technology And Control 49, no. 1 (2020): 55–79. http://dx.doi.org/10.5755/j01.itc.49.1.24140.

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A biometric identification of persons wchich utilize contour of a human hand belogs to still very interesting and still not totally explored areas and its accuracy and effectiveness depends on technical capabilities to some extent. Presented paper solves given problem using combination of different algorithms. A hand contour is used, topological description of the hand, evolutionary algorithm, algorithm linear regression to estimate the knuckles positions and for contours comparison is used an algorithm Iterative Closest Point (ICP) in its genuine shape. All 5 fingers is at computer classifica

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Parent, Benjamin, Annemarie Kökösy, and Dragos Horvath. "Optimized Evolutionary Strategies in Conformational Sampling." Soft Computing 11, no. 1 (2006): 63–79. http://dx.doi.org/10.1007/s00500-006-0053-y.

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Zhou, Tong, J. M. Carlson, and John Doyle. "Evolutionary dynamics and highly optimized tolerance." Journal of Theoretical Biology 236, no. 4 (2005): 438–47. http://dx.doi.org/10.1016/j.jtbi.2005.03.023.

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32

Gao, Cong, Zhongbo Hu, and Wangyu Tong. "Linear prediction evolution algorithm: a simplest evolutionary optimizer." Memetic Computing 13, no. 3 (2021): 319–39. http://dx.doi.org/10.1007/s12293-021-00340-x.

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Szczepanik, M., and T. Burczyński. "Swarm optimization of stiffeners locations in 2-D structures." Bulletin of the Polish Academy of Sciences: Technical Sciences 60, no. 2 (2012): 241–46. http://dx.doi.org/10.2478/v10175-012-0032-7.

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Abstract. The paper is devoted to the application of the swarm methods and the finite element method to optimization of the stiffeners location in the 2-D structures (plane stress, bending plates and shells). The structures are optimized for the stress and displacement criteria. The numerical examples demonstrate that the method based on the swarm computation is an effective technique for solving the computer aided optimal design. The additional comparisons of the effectiveness of the particle swarm optimizer (PSO) and evolutionary algorithms (EA) are presented.

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FATHIANPOUR, A., and S. SEYEDTABAII. "EVOLUTIONARY SEARCH FOR OPTIMIZED LNA COMPONENTS GEOMETRY." Journal of Circuits, Systems and Computers 23, no. 01 (2014): 1450011. http://dx.doi.org/10.1142/s021812661450011x.

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In this paper, an optimized design procedure based on genetic algorithm (GA) for automatic synthesis of dual-band concurrent fully integrated low-noise amplifiers (LNA) targeted to 802.16d @ 3.5 GHz and 802.11b, g @ 2.4 GHz standards is discussed. The algorithm delivers the circuit elements geometry, rather than their values, and bias levels to secure the best level of LNA gain, input matching, output matching and power consumption. Working on the components geometry level aims at considering the elements parasitic effects. The basic cascode and a current reuse folded cascode LNA's are tried.

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Szécsi, Dorottya, Poojan Agrawal, Richard Wünsch, and Norbert Langer. "Bonn Optimized Stellar Tracks (BoOST)." Astronomy & Astrophysics 658 (February 2022): A125. http://dx.doi.org/10.1051/0004-6361/202141536.

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Massive and very massive stars can play important roles in stellar populations by ejecting strong stellar winds and exploding in energetic phenomena. It is therefore imperative that their behavior be properly accounted for in synthetic model populations. We present nine grids of stellar evolutionary model sequences, together with finely resolved interpolated sequences and synthetic populations, of stars with 9–500 M⊙ and with metallicities ranging from Galactic metallicity down to 1/250 Z⊙. The stellar models were computed with the Bonn evolutionary code with consistent physical ingredients, a

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Li, Kangshun, Zhichao Wen, Zhaopeng Wang, and Shen Li. "Optimised placement of wireless sensor networks by evolutionary algorithm." International Journal of Computational Science and Engineering 15, no. 1/2 (2017): 74. http://dx.doi.org/10.1504/ijcse.2017.085995.

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Wen, Zhichao, Zhaopeng Wang, Kangshun Li, and Shen Li. "Optimised placement of wireless sensor networks by evolutionary algorithm." International Journal of Computational Science and Engineering 15, no. 1/2 (2017): 74. http://dx.doi.org/10.1504/ijcse.2017.10006999.

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Zaman, Fawad, and Ijaz Mansoor Qureshi. "5D Parameter Estimation of Near-Field Sources Using Hybrid Evolutionary Computational Techniques." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/310875.

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Hybrid evolutionary computational technique is developed to jointly estimate the amplitude, frequency, range, and 2D direction of arrival (elevation and azimuth angles) of near-field sources impinging on centrosymmetric cross array. Specifically, genetic algorithm is used as a global optimizer, whereas pattern search and interior point algorithms are employed as rapid local search optimizers. For this, a new multiobjective fitness function is constructed, which is the combination of mean square error and correlation between the normalized desired and estimated vectors. The performance of the p

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De Falco, Ivanoe, Umberto Scafuri, and Ernesto Tarantino. "Optimizing Personalized Touristic Itineraries by a Multiobjective Evolutionary Algorithm." International Journal of Information Technology & Decision Making 15, no. 06 (2016): 1269–312. http://dx.doi.org/10.1142/s0219622016500413.

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The paper presents an electronic tourist guide, relying on an evolutionary optimizer, able to plan personalized multiple-day itineraries by considering several contrasting objectives. Since the itinerary planning can be modeled as an extension of the NP-complete team orienteering problem with time windows, a multiobjective evolutionary optimizer is proposed to find in reasonable times near-optimal solutions to such an extension. This optimizer automatically designs the itinerary by aiming at maximizing the tourists’ satisfaction as a function of their personal preferences and environmental con

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Sanchez-Montero, Rocio, Sancho Salcedo-Sanz, J. A. Portilla-Figueras, and Richard Langley. "HYBRID PIFA-PATCH ANTENNA OPTIMIZED BY EVOLUTIONARY PROGRAMMING." Progress In Electromagnetics Research 108 (2010): 221–34. http://dx.doi.org/10.2528/pier10072804.

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Dees, Nathan D., and Sonya Bahar. "Mutation Size Optimizes Speciation in an Evolutionary Model." PLoS ONE 5, no. 8 (2010): e11952. http://dx.doi.org/10.1371/journal.pone.0011952.

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Shiroie, Masoud, and Karim Mohammadi. "Optimized Asynchronous Circuit Design based on Evolutionary Algorithm." International Journal of Computer Applications 40, no. 4 (2012): 1–6. http://dx.doi.org/10.5120/5029-7177.

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Dang, Duc-Cuong, Anton Eremeev, and Per Kristian Lehre. "Escaping Local Optima with Non-Elitist Evolutionary Algorithms." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 12275–83. http://dx.doi.org/10.1609/aaai.v35i14.17457.

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Most discrete evolutionary algorithms (EAs) implement elitism, meaning that they make the biologically implausible assumption that the fittest individuals never die. While elitism favours exploitation and ensures that the best seen solutions are not lost, it has been widely conjectured that non-elitism is necessary to explore promising fitness valleys without getting stuck in local optima. Determining when non-elitist EAs outperform elitist EAs has been one of the most fundamental open problems in evolutionary computation. A recent analysis of a non-elitist EA shows that this algorithm does no

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Abudhahir, A., and S. Baskar. "Evolutionary optimised nonlinear function for linearisation of constant temperature anemometer." IET Science, Measurement & Technology 2, no. 4 (2008): 208–18. http://dx.doi.org/10.1049/iet-smt:20070048.

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Zhang, Hong, and Masumi Ishikawa. "The performance verification of an evolutionary canonical particle swarm optimizer." Neural Networks 23, no. 4 (2010): 510–16. http://dx.doi.org/10.1016/j.neunet.2009.12.002.

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Shakya, S., M. Kern, G. Owusu, and C. M. Chin. "Neural network demand models and evolutionary optimisers for dynamic pricing." Knowledge-Based Systems 29 (May 2012): 44–53. http://dx.doi.org/10.1016/j.knosys.2011.06.023.

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CHIBA, Kazuhisa. "Evolutionary-Based Hybrid Optimizer Applicable to Large-Scale Design Problems." Journal of Computational Science and Technology 7, no. 1 (2013): 28–37. http://dx.doi.org/10.1299/jcst.7.28.

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Xiaohong, Qiu, and Qiu Xiaohui. "An Evolutionary Particle Swarm Optimizer Based on Fractal Brownian Motion." Journal of Computational Intelligence and Electronic Systems 1, no. 1 (2012): 138–43. http://dx.doi.org/10.1166/jcies.2012.1016.

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Gómez-Iglesias, Antonio, Miguel A. Vega-Rodríguez, Francisco Castejón-Magaña, Miguel Cárdenas-Montes, and Enrique Morales-Ramos. "Evolutionary computation and grid computing to optimise nuclear fusion devices." Cluster Computing 12, no. 4 (2009): 439–48. http://dx.doi.org/10.1007/s10586-009-0101-3.

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Hu, Yabao, Hanning Chen, Maowei He, Liling Sun, Rui Liu, and Hai Shen. "Multi-Swarm Multi-Objective Optimizer Based on p-Optimality Criteria for Multi-Objective Portfolio Management." Mathematical Problems in Engineering 2019 (January 21, 2019): 1–22. http://dx.doi.org/10.1155/2019/8418369.

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Portfolio management is an important technology for reasonable investment, fund management, optimal asset allocation, and effective investment. Portfolio optimization problem (POP) has been recognized as an NP-hard problem involving numerous objectives as well as constraints. Applications of evolutionary algorithms and swarm intelligence optimizers for resolving multi-objective POP (MOPOP) have attracted considerable attention of researchers, yet their solutions usually convert MOPOP to POP by means of weighted coefficient method. In this paper, a multi-swarm multi-objective optimizer based on

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