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A new taxonomy of global optimization algorithms

NATURAL COMPUTING 2022年第2期21卷 219-242页

作者： Stork, Joerg Eiben, A. E. Bartz-Beielstein, Thomas TH Koln Steinmullerallee 1 D-51643 Gummersbach Germany Vrije Univ Amsterdam NL-1081 HV Amsterdam Netherlands

Surrogate-based optimization, nature-inspired metaheuristics, and hybrid combinations have become state of the art in algorithm design for solving real-world optimization problems. Still, it is difficult for practitioners to get an overview that explains their advantages in comparison to a large number of available methods in the scope of optimization. Available taxonomies lack the embedding of current approaches in the larger context of this broad field. This article presents a taxonomy of the field, which explores and matches algorithm strategies by extracting similarities and differences in their search strategies. A particular focus lies on algorithms using surrogates, nature-inspired designs, and those created by automatic algorithm generation. The extracted features of algorithms, their main concepts, and search operators, allow us to create a set of classification indicators to distinguish between a small number of classes. The features allow a deeper understanding of components of the search strategies and further indicate the close connections between the different algorithm designs. We present intuitive analogies to explain the basic principles of the search algorithms, particularly useful for novices in this research field. Furthermore, this taxonomy allows recommendations for the applicability of the corresponding algorithms.

关键词： Metaheuristics Surrogate Hybrid optimization evolutionary computation Taxonomy

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Robust Tuning of Multiresonant Current Controllers for Grid-Tied Converters and Erroneous Use of the Naslin Polynomial Method

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IEEE ACCESS 2022年 10卷 88211-88225页

作者： Ufnalski, Bartlomiej Michalczuk, Marek Galecki, Andrzej Warsaw Univ Technol Fac Elect Engn Inst Control & Ind Elect PL-00662 Warsaw Poland

The majority of the paper is devoted to debunking flawed methods proposed in the literature within the context of multiresonant control systems for grid-connected converters and then a novel approach is proposed. Several flawed approaches to grid tied power electronic converters (rectifiers and shunt active power filters) are discussed to initiate a critical discussion regarding some tuning methods reported in the topical literature. In this paper we analytically show that some of them are even erroneous from the control theory point of view or are based on mathematically erroneous derivations. That is especially prevalent for papers in which the Naslin polynomial method is employed to tune proportional-multiresonant controllers. Some authors recognize that the resulting control systems are not robust, i.e. not practical, and use obtained gains only as a starting point for further tuning using the trial and error method or evolutionary global optimization. Robustness of such systems is often not guaranteed and if it occurs at all, it is by chance or thanks to a combination of luck and expert knowledge of the engineer (not by deliberate design). Therefore, the absence of such a guarantee motivates the search for better ways to tune such controllers. As a result, a practical robust controller tuning method for multiresonant grid current controllers is proposed and verified experimentally. We are convinced that one of the solutions can be based on the disk margin stability analysis and a global search algorithm. The required robustness is expressed as stability margins. The design procedure is very user-friendly and the disk size is the key design parameter to be selected by an engineer. The practicality of the tuning method is demonstrated empirically in a 10 kVA physical grid-tied converter.

关键词： Voltage control Tuning Power harmonic filters Harmonic analysis Robustness Pulse width modulation Phase locked loops Power grids Power converters Robust control evolutionary computation Grid-tied converter multiresonant controllers robust control Naslin polynomial method disk margin analysis evolutionary global optimization

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Multi-objective approaches to portfolio optimization with market impact costs

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MEMETIC COMPUTING 2022年第4期14卷 411-421页

作者： Wang, Hongze Li, Xuerong Hong, Wenjing Tang, Ke Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

Market impact costs are important factors to portfolio management, which always lead to adverse price fluctuations in trading. As the practical trading volume becomes increasingly large, the Problem of Portfolio Optimization with Market Impact Costs (MICPOP) has become more important. Traditional MICPOPs involve seeking an optimal allocation of capital to a limited number of assets with respect to either additional constraints or a weighted sum of objectives (net return, investment risk). We suggest solving MICPOPs with Multi-Objective evolutionary Algorithms (MOEAs). Specifically, we formulate MICPOPs as a bi-objective optimization problem. The advantages of MOEAs over state-of-the-art single objective approaches to MICPOPs will be shown through empirical studies. Our study has revealed that a well-known MOEA, namely Nondominated Sorting Genetic Algorithm II (NSGA-II), fails to provide satisfactory solution quality sometimes. Hence, a memetic MOEA for Portfolio Optimization with Market Impact costs (POMI-MOEA), which inherits the global search capability of NSGA-II while introducing a new local search operator, is proposed and evaluated in this paper. Comprehensive experimental studies on 11 portfolio cases have shown the superiority of POMI-MOEA over NSGA-II and other two MOEAs for MICPOPs.

关键词： Portfolio optimization Market impact cost Multi-objective optimization evolutionary computation Memetic algorithm

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Experiments in evolutionary image enhancement with ELAINE

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2022年第4期23卷 557-579页

作者： Correia, Joao Lopes, Daniel Vieira, Leonardo Rodriguez-Fernandez, Nereida Carballal, Adrian Romero, Juan Machado, Penousal Univ Coimbra Ctr Informat & Syst Dept Informat Engn Coimbra Portugal Univ A Coruna CITIC Res Ctr Informat & Commun Technol Coruna Spain

Image enhancement is an image processing procedure in which the image's original information is refined, for example by highlighting specific features to ease post-processing analyses by a human or machine. This procedure remains challenging since each set of images is often taken under diverse conditions which makes it hard to find an image enhancement solution that fits all conditions. State-of-the-art image enhancement pipelines apply filters that solve specific issues;therefore, it is still hard to generalise these pipelines to all types of problems encountered. We have recently introduced a Genetic Programming approach named ELAINE (evolutionary Image eNhancEment) for evolving image enhancement pipelines based on pre-defined image filters. In this paper, we showcase its potential to create solutions under a real-estate marketing scenario by comparing it with a manual approach and an existing tool for automatic image enhancement. The ELAINE obtained results far exceed those obtained by manual combinations of filters and by the one-click method, in all the metrics explored. We further explore the potential of creating non-photorealistic effects by applying the evolved pipelines to different types of images. The results highlight ELAINE's potential to transform input images into either suitable real-estate images or non-photorealistic renderings, thus transforming contents and possibly enhancing its aesthetic appeal.

关键词： Image enhancement Image processing Computer vision evolutionary computation Genetic programming

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DSGA: A Distributed Segment-Based Genetic Algorithm for Multi-Objective Outsourced Database Partitioning

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INFORMATION SCIENCES 2022年 612卷 864-886页

作者： Ge, Yong-Feng Zhan, Zhi-Hui Cao, Jinli Wang, Hua Zhang, Yanchun Lai, Kuei-Kuei Zhang, Jun Victoria Univ Inst Sustainable Ind & Liveable Cities Melbourne 3011 Australia South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China La Trobe Univ Dept Comp Sci & Informat Technol Melbourne 3083 Australia Peng Cheng Lab Shenzhen 518055 Peoples R China Guangzhou Univ Cyberspace Inst Adv Technol Guangzhou 510006 Peoples R China Chaoyang Univ Technol Dept Business Adm Taichung 413 Taiwan Hanyang Univ Ansan 15588 South Korea

The outsourced distributed database is frequently used to tackle the large amounts of data in various smart city scenarios. The data partition technique is a significant research topic in the outsourced distributed database because it can directly affect the database perfor-mance in data exchange and sharing. Multiple objectives, including communication cost, load balance, and data privacy, should be considered during data partition. Previous approaches remain narrow in dealing with one of these objectives. However, the emphasis on any single objective cannot improve the entire performance of the outsourced dis-tributed database. In this paper, a distributed segment-based genetic algorithm (DSGA) is proposed, which can protect data privacy as well as achieve the trade-off between com-munication cost and load balance. For privacy protection, a digit-based anonymity strategy is proposed based on the characteristic of attributes, which can maintain information integrity and achieve fuzzy identification. After that, a three-layer distributed framework is proposed for the multi-objective optimization to enhance the search efficiency and to achieve the trade-off between communication cost and load balance. Specifically, two segment-based operators, i.e., segment-based recombination and segment-based muta-tion, are proposed to sufficiently exchange evolutionary information to accelerate the con-vergence speed and to maintain population diversity to cover the whole multi-objective space, respectively. The performance of the proposed DSGA is verified in terms of solution accuracy and convergence speed. The effect of proposed strategies and operators in DSGA is also confirmed.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Multi-objective optimization evolutionary computation Database partitioning Distributed genetic algorithm Smart cities

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Chaotic Mapping Based Advanced Aquila Optimizer With Single Stage evolutionary Algorithm

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IEEE ACCESS 2022年 10卷 89153-89169页

作者： Verma, Monika Sreejeth, Mini Singh, Madhusudan Babu, Thanikanti Sudhakar Alhelou, Hassan Haes Delhi Technol Univ Dept Elect Engn New Delhi 110042 India Chaitanya Bharathi Inst Technol Dept Elect & Elect Engn Hyderabad 500075 India Tishreen Univ Fac Mech & Elect Engn Dept Elect Power Engn Latakia 2230 Syria

The intelligent optimization techniques have been introduced by carefully observing the behavior of various hunters like a whale, grey wolf, Aquila, and lizards for estimating global optimum solutions in fair time by forming appropriate mathematical models. However, hunting-based algorithms suffer from slow and pre-requisite convergence and get caught up in local minimum. Aquila optimizer (AO) is one of the recently developed hunting-based methods that encounter a similar type of shortcoming in a few situations. This research introduces the concept of chaotic mapping to the standard AO in order to increase the convergence speed. Also to maintain the balance of exploration performed by AO with its exploitation capability, a single stage evolutionary algorithm is also integrated with it. The performance of standard AO and modified AO are tested for well-defined unimodal and multimodal Benchmark functions. The proposed framework produces one population by standard AO and a new population by single stage genetic algorithm based evolutionary concept in which binary tournament selection, roulette wheel selection, shuffle crossing over and displacement mutation occur to generate a new population. The chaotic mapping criteria are then applied to obtain various variants of the standard AO technique. The general results obtained from the proposed novel chaotic mapping-based advanced AO with single stage evolutionary algorithm shows that it outperforms the standard AO. This advanced technique is thus applied to real-world design engineering problems to study its significance from an industrial point of view.

关键词： Genetic algorithms Statistics Behavioral sciences Chaos evolutionary computation Convergence Metaheuristics Hunting-based algorithm evolutionary algorithm Aquila optimizer (AO) chaotic maps

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On Multimodal Microblog Summarization

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IEEE TRANSACTIONS ON computationAL SOCIAL SYSTEMS 2022年第5期9卷 1317-1329页

作者： Saini, Naveen Saha, Sriparna Bhattacharyya, Pushpak Mrinal, Shubhankar Mishra, Santosh Kumar Woosong Univ Endicott Coll Int Studies Technol Studies Dept Daejeon 300718 South Korea IIT Patna Dept Comp Sci & Engn Patna 801106 Bihar India Dept Comp Sci & Engn IIT Bombay Mumbai 400076 Maharashtra India NIT Durgapur Dept Comp Sci & Engn Durgapur 713209 India

Microblog summarization systems are gaining importance during natural disasters. A lot of tweets are posted along with multimedia content during the occurrence of any natural disaster event. Extracting relevant information/summary from these tweets is important for the smooth functioning of the rescue operation. Moreover, because of the limited size of the tweets, in many cases, tweets are associated with images. The current work is the first of its kind where both the image and the tweet text are utilized simultaneously to generate a summary from microblog data generated during a disaster event. Different aspects, such as syntactic similarity, the maximum length of the tweets, retweet score, and antiredundancy, are considered as objective functions and those are simultaneously optimized using a metaheuristic population-based evolutionary strategy to select a good set of tweets to form a good quality summary. In order to extract information from images, a dense captioning model is utilized and the dense captions are further utilized for calculating the antiredundancy measure. We employed word mover distance to capture the semantic similarity between two tweets. Due to the unavailability of the dataset for multimodal microblog summarization tasks in a disaster-event scenario, datasets are created and made openly available to the community. The obtained summarization results are evaluated using the well-known ROUGE measure.

关键词： Blogs Task analysis Social networking (online) evolutionary computation Data mining Clustering algorithms Visualization Disaster events evolutionary algorithm extractive summarization image dense captioning microblog multimodal multiobjective optimization (MOO)

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Solving Multitask Optimization Problems With Adaptive Knowledge Transfer via Anomaly Detection

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IEEE TRANSACTIONS ON evolutionary computation 2022年第2期26卷 304-318页

作者： Wang, Chao Liu, Jing Wu, Kai Wu, Zhaoyang Xidian Univ Guangzhou Inst Technol Guangzhou 710071 Peoples R China Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China

evolutionary multitask optimization (EMTO) has recently attracted widespread attention in the evolutionary computation community, which solves two or more tasks simultaneously to improve the convergence characteristics of tasks when individually optimized. Effective knowledge between tasks is transferred by taking advantage of the parallelism of population-based search. Without any prior knowledge about tasks, it is a challenging problem of how to adaptively transfer effective knowledge between tasks and reduce the impact of negative transfer in EMTO. However, these two issues are rarely studied simultaneously in the existing literature. Besides, in complex many-task environments, the potential relationships among individuals from highly diverse populations associated with tasks directly determine the effectiveness of cross-task knowledge transfer. Keeping those in mind, we propose a multitask evolutionary algorithm based on anomaly detection (MTEA-AD). Specifically, each task is assigned a population and an anomaly detection model. Each anomaly detection model is used to learn the relationship among individuals between the current task and the other tasks online. Individuals that may carry negative knowledge are identified as outliers, and candidate transferred individuals identified by the anomaly detection model are selected to assist the current task, which may carry common knowledge across the current task and other tasks. Furthermore, to realize the adaptive control of the degree of knowledge transfer, the successfully transferred individuals that survive to the next generation through the elitism are used to update the anomaly detection parameter. The fair competition between offspring and candidate transferred individuals can effectively reduce the risk of negative transfer. Finally, the empirical studies on a series of synthetic benchmarks and a practical study are conducted to verify the effectiveness of MTEA-AD. The experimental results demonstrate that o

关键词： Task analysis Optimization Statistics Sociology Knowledge transfer Anomaly detection evolutionary computation Adaptive strategy anomaly detection evolutionary multitask optimization (EMTO) knowledge transfer many-task optimization (MaTO)

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Constraint estimation in three-diode solar photovoltaic model using Gaussian and Cauchy mutation-based hunger games search optimizer and enhanced Newton-Raphson method

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IET RENEWABLE POWER GENERATION 2022年第8期16卷 1733-1772页

作者： Premkumar, Manoharan Jangir, Pradeep Kumar, Chandrasekaran Jebaseelan, Somasundaram David Thanasingh Sundarsingh Alhelou, Hassan Haes Elavarasan, Rajvikram Madurai Chen, Huiling Dayananda Sagar Coll Engn Dept Elect & Elect Engn Bengaluru 560078 Karnataka India Rajasthan Rajya Vidyut Prasaran Nigam Ltd Power & Energy Sect Sikar 332025 Rajasthan India M Kumarasamy Coll Engn Dept Elect & Elect Engn Karur 639113 Tamil Nadu India Sathyabama Inst Sci & Technol Dept Elect & Elect Engn Chennai 600119 Tamil Nadu India Tishreen Univ Dept Elect Power Engn Latakia 2230 Syria Thiagarajar Coll Engn Dept Elect & Elect Engn Madurai 625015 Tamil Nadu India Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

The reliability of the photovoltaic models is strongly reliant on their parameters, which are primarily determined by the optimization algorithm and the objective function. As a result, obtaining the parameters under different environmental conditions is critical for increasing their performance, reliability and significantly lowering cost. Many optimization techniques are reported to address this problem based on the complexity. As a result, an enhanced version of the recently reported Hunger Games Search Optimizer (HGSO) method called Gaussian and Cauchy Mutation-based HGSO (GCMHGSO) algorithm for defining the requirements of the Three-Diode equivalent Model (TDeM) by utilizing multiple representations in the algorithm along with an efficient objective function. The Cauchy mutation increases the exploration ability, and Gaussian mutation increases the exploitation ability of the basic HGSO. Furthermore, an Enhanced Newton-Raphson Method (ENRM) is presented to effectively solve the behaviour of the current-voltage relation of the TDeM. The robust optimization is also considered to demonstrate the impact of the measurement error. Comparing the GCMHGSO-ENRM to other competitors reveals that the proposed GCMHGSO-ENRM can accurately find the best solution, and its effectiveness is verified in many statistical parameters. It is found that the GCMHGSO-ENRM algorithm is stable and robust compared to other competitors.

关键词： constraint estimation photovoltaic power systems Gaussian mutation efficient objective function Three-Diode equivalent Model recently reported Hunger Games Search Optimizer method optimisation Enhanced Newton-Raphson Method robust optimization TDeM Cauchy Mutation Cauchy mutation metaheuristics optimization techniques Newton-Raphson method enhanced Newton-Raphson method statistical parameters evolutionary computation Gaussian processes photovoltaic models search problems GCMHGSO-ENRM algorithm three-diode solar photovoltaic model basic HGSO optimization algorithm

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A Multiobjective evolutionary Algorithm Based on Objective-Space Localization Selection

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第5期52卷 3888-3901页

作者： Zhou, Yuren Chen, Zefeng Huang, Zhengxin Xiang, Yi Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510006 Peoples R China Sun Yat Sen Univ Key Lab Machine Intelligence & Adv Comp Minist Educ Guangzhou 510006 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Youjiang Med Univ Nationalities Dept Comp Sci Baise 533000 Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510006 Peoples R China

This article proposes a simple yet effective multiobjective evolutionary algorithm (EA) for dealing with problems with irregular Pareto front. The proposed algorithm does not need to deal with the issues of predefining weight vectors and calculating indicators in the search process. It is mainly based on the thought of adaptively selecting multiple promising search directions according to crowdedness information in local objective spaces. Concretely, the proposed algorithm attempts to dynamically delete an individual of poor quality until enough individuals survive into the next generation. In this environmental selection process, the proposed algorithm considers two or three individuals in the most crowded area, which is determined by the local information in objective space, according to a probability selection mechanism, and deletes the worst of them from the current population. Thus, these surviving individuals are representative of promising search directions. The performance of the proposed algorithm is verified and compared with seven state-of-the-art algorithms [including four general multi/many-objective EAs and three algorithms specially designed for dealing with problems with irregular Pareto-optimal front (PF)] on a variety of complicated problems with different numbers of objectives ranging from 2 to 15. Empirical results demonstrate that the proposed algorithm has a strong competitiveness power in terms of both the performance and the algorithm compactness, and it can well deal with different types of problems with irregular PF and problems with different numbers of objectives.

关键词： Sociology Statistics evolutionary computation Optimization Heuristic algorithms Search problems Computer science evolutionary algorithm (EA) irregular Pareto front multiobjective optimization projection

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