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multi-objective pity beetle algorithm based optimal control of wastewater treatment process

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2023年 170卷 188-206页

作者： Du, Xianjun Peng, Yu Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China Lanzhou Univ Technol Key Lab Gansu Adv Control Ind Proc Lanzhou 730050 Peoples R China Lanzhou Univ Technol Natl Demonstrat Ctr Expt Elect & Control Engn Educ Lanzhou 730050 Peoples R China Lanzhou Univ Technol 287 Langongping Rd Lanzhou 730050 Gansu Peoples R China

Water is a critically scarce resource for industrial production and social life. The discharge of untreated wastewater into natural waters can pose a serious risk to human health. Most of urban wastewater treatment plants use biochemical methods, the most common of which is the biological reaction through activated sludge to degrade pollutants. Considering the public environmental protection and socio-economic needs, this paper es-tablishes a wastewater treatment process (WWTP) optimization model considering the trade-off between effluent quality (EQ) and energy consumption (EC), and designs an dynamic optimal control framework based on a novel multi-objective evolutionary algorithm (MOEA) to track and control the key variables in the WWTP. The nu-merical experiments of multi-objective test functions show that the proposed MOEA has good convergence and distributivity. Simulation results based on the BSM1 platform show that the constructed framework can accu-rately adjust the set-points of controllers in time to improve the performance, which has broad application prospects in practical applications.

关键词： Evolutionary algorithm dynamic multi-objective optimization Optimal process control Pity beetle algorithm Population prediction Wastewater treatment process

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Population-based metaheuristics for continuous boundary-constrained dynamic multi-objective optimisation problems

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SWARM AND EVOLUTIONARY COMPUTATION 2014年 14卷 31-47页

作者： Helbig, Marde Engelbrecht, Andries R. Meraka Inst CSIR Scientia ZA-0184 Pretoria South Africa Univ Pretoria Dept Comp Sci ZA-0002 Pretoria South Africa

Most real-world optimisation problems are dynamic in nature with more than one objective, where at least two of these objectives are in conflict with one another. This kind of problems is referred to as dynamic multi-objective optimisation problems (DMOOPs). Most research in multi-objective optimisation (MOO) have focussed on static MOO (SMOO) and dynamic single-objective optimisation. However, in recent years, algorithms were proposed to solve dynamic MOO (DMOO). This paper provides an overview of the algorithms that were proposed in the literature to solve DMOOPs. In addition, challenges, practical aspects and possible future research directions of DMOO are discussed. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Population-based algorithms dynamic multi-objective optimization dynamic multi-objective optimization algorithms

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A dynamic multi-objective evolutionary algorithm based on prediction

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第1期10卷 1-15页

作者： Wu, Fei Chen, Jiacheng Wang, Wanliang Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China

The dynamic multi-objective optimization problem (DMOP) is a common problem in optimization problems;the main reasons are the objective's conflict and environment changes. In this paper, we provide a prediction approach based on diversity screening and special point prediction (DSSP) to tackle the dynamic optimization issue. First, we introduce a decision variable clustering and screening strategy that clusters the decision space of the non-dominated solution set to find the cluster centroids and then employs a decision variable screening strategy to filter out solutions that have an impact on the distribution of individuals. This approach can broaden the range of dynamic multi-objective optimization algorithms. Second, an approach for predicting special points is suggested. The algorithm's convergence is improved following environmental changes by forecasting the special point tracking Pareto front in the object space. Finally, the forward-looking center points are used to predict the non-dominated solution set and eliminate the useless individuals in the population. The prediction strategy can help the solution set converge while maintaining its diversity, which is compared with the four other state-of-the-art strategies. Our experimental results demonstrate that the proposed algorithm, DSSP, can effectively tackle DMOPs.

关键词： dynamic multi-objective optimization prediction diversity maintenance strategy knee point close-to-boundary point convergence

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Balancing energy consumption and risk of delay in high speed trains: A three-objective real-time eco-driving algorithm with fuzzy parameters

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2018年 95卷 652-678页

作者： Fernandez-Rodriguez, Adrian Fernandez-Cardador, Antonio Cucala, Asuncion P. Comillas Pontifical Univ Inst Res Technol ICAI Sch Engn 23 Alberto Aguilera St Madrid 28015 Spain

Eco-driving is an energy efficient traffic operation measure that may lead to important energy savings in high speed railway lines. When a delay arises in real time, it is necessary to recalculate an optimal driving that must be energy efficient and computationally efficient. In addition, it is important that the algorithm includes the existing uncertainty associated with the manual execution of the driving parameters and with the possible future traffic disturbances that could lead to new delays. This paper proposes a new algorithm to be executed in real time, which models the uncertainty in manual driving by means of fuzzy numbers. It is a multi-objective optimization algorithm that includes the classical objectives in literature, running time and energy consumption, and as well a newly defined objective, the risk of delay in arrival. The risk of delay in arrival measure is based on the evolution of the time margin of the train up to destination. The proposed approach is a dynamic algorithm designed to improve the computational time. The optimal Pareto front is continuously tracked during the train travel, and a new set of driving commands is selected and presented to the driver when a delay is detected. The algorithm evaluates the 3 objectives of each solution using a detailed simulator of high speed trains to ensure that solutions are realistic, accurate and applicable by the driver. The use of this algorithm provides energy savings and, in addition, it permits railway operators to balance energy consumption and risk of delays in arrival. This way, the energy performance of the system is improved without degrading the quality of the service.

关键词： Delay recovery dynamic multi-objective optimization Eco-driving Fuzzy logic High speed railway Simulation

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Solving dynamic multi-objective problems with an evolutionary multi-directional search approach

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KNOWLEDGE-BASED SYSTEMS 2020年 194卷

作者： Hu, Yaru Ou, Junwei Zheng, Jinhua Zou, Juan Yang, Shengxiang Ruan, Gan Xiangtan Univ Dept Math & Computat Sci Xiangtan 411105 Hunan Peoples R China Xiangtan Univ Minist Educ Key Lab Intelligent Comp & Informat Proc Xiangtan 411105 Hunan Peoples R China Hengyang Normal Univ Hunan Prov Key Lab Intelligent Informat Proc & Ap Hengyang 421002 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England

The challenge of solving dynamic multi-objective optimization problems is to effectively and efficiently trace the varying Pareto optimal front and/or Pareto optimal set. To this end, this paper proposes a multi-direction search strategy, aimed at finding the dynamic Pareto optimal front and/or Pareto optimal set as quickly and accurately as possible before the next environmental change occurs. The proposed method adopts a multi-directional search approach which mainly includes two parts: an improved local search and a global search. The first part uses individuals from the current population to produce solutions along each decision variables direction within a certain range and updates the population using the generated solutions. As a result, the first strategy enhances the convergence of the population. In part two, individuals are generated in a specific random method along every dimensions orientation in the decision variable space, so as to achieve good diversity as well as guarantee the avoidance of local optimal solutions. The proposed algorithm is measured on several benchmark test suites with various dynamic characteristics and different difficulties. Experimental results show that this algorithm is very competitive in dealing with dynamic multi-objective optimization problems when compared with four state-of-the-art approaches. (C) 2019 Elsevier B.V. All rights reserved.

关键词： dynamic dynamic multi-objective optimization Local search multi-directional search strategy

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dynamic multi-objective AWPSO in DT-Assisted UAV Cooperative Task Assignment

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2023年第10期41卷 3444-3460页

作者： Deng, Min Yao, Zhiqiang Li, Xingwang Wang, Han Nallanathan, Arumugam Zhang, Zeyang Xiangtan Univ Sch Automat & Elect Informat Hunan Natl Ctr Appl Math Key Lab Credible Intelligent Nav & Positioning Hu Xiangtan 411105 Peoples R China Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan 411105 Peoples R China Henan Polytech Univ Sch Phys & Elect Informat Engn Jiaozuo 454000 Henan Peoples R China Yichun Univ Coll Phys Sci & Engn Yichun 336000 Peoples R China Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4NS England

In recent years, more and more attention has been paid to the unmanned aerial vehicle (UAV) cooperative task assignment. In order to complete the task with the lowest cost, some researchers use multi-objective optimization to solve the assignment problem. But few of them consider the complex dynamic scenarios. In this article, the time-varying resource supply and demands are provided by established digital twins (DTs) of UAVs and targets, thereby enabling accurate decision guidance for dynamic task assignment. It takes the scheduling cost, path cost, risk cost and total task time cost as the optimization objectives. To solve this model, an improved dynamic multi-objective adaptive weighted particle swarm optimization algorithm (DMOAWPSO) is proposed. In the initialization stage, a heuristic method is used to increase the effectiveness of the solution. Besides, the adaptive mutation and subgroup methods are adopted to improve the diversity of the solution. Then, effective environment change detection and response strategies are designed to adapt to dynamic scenarios. Finally, the evaluation metrics are calculated in different instances. Compared with the popular and classic dynamic multi-objective algorithms, the simulation results verify that the proposed algorithm is effective and can cope with the environment changes better in solving the task assignment *** recent years, more and more attention has been paid to the unmanned aerial vehicle (UAV) cooperative task assignment. In order to complete the task with the lowest cost, some researchers use multi-objective optimization to solve the assignment problem. But few of them consider the complex dynamic scenarios. In this article, the time-varying resource supply and demands are provided by established digital twins (DTs) of UAVs and targets, thereby enabling accurate decision guidance for dynamic task assignment. It takes the scheduling cost, path cost, risk cost and total task time cost as the optimization o

关键词： dynamic multi-objective optimization UAV digital twin task assignment evolutionary algorithm

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On the design of a framework integrating an optimization engine with streaming technologies

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 107卷 538-550页

作者： Barba-Gonzalez, Cristobal Nebro, Antonio J. Benitez-Hidalgo, Antonio Garcia-Nieto, Jose Aldana-Montes, Jose F. Univ Malaga Dept Lenguajes & Ciencias Computat Ada Byron Res Bldg E-29071 Malaga Spain

A number of streaming technologies have appeared in the last years as a result of the rising of Big Data applications. Nowadays, deciding which technology to adopt is not an easy task due not only to the number of available data streaming processing projects, but also because they are continuously evolving. In this paper, we focus on how these issues have affected jMetalSP, a framework for dynamic multi-objective optimization that incorporates streaming features. jMetalSP allows the development of three tier optimization workflows where the central component is an optimizer that is continuously solving a dynamic multi-objective optimization problem. This problem can change as a consequence of the analysis of data streams carried out by components that use the Apache Spark streaming engine. A third kind of components receive and process the Pareto front approximations being yielded by the optimization algorithm. However, all jMetalSP elements are tightly coupled and linked to Spark, making it difficult to use a different streaming system. To overcome this issue, we have redesigned the jMetalSP architecture to make it flexible enough to avoid the dependence of any particular streaming system. This way, popular Apache projects such as Spark Structured Streaming, Kafka Streams, or Flink can be used without requiring to change the rest of components of the application. Furthermore, Kafka can be used for inter-process communication, what enables the execution of components in different nodes of a cluster, independently of their implementation languages thanks to the serialization of data streams with Apache Avro. We show how the embraced solution provides a high degree of flexibility that enhances the usability of jMetalSP. To this end, a representative case study based on a transport problem is conducted that focuses on data representation and performance evaluation of the Spark, Flink, and Kafka systems. (C) 2020 Elsevier B.V. All rights reserved.

关键词： dynamic multi-objective optimization Streaming data processing Big data Software framework

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Considering spatiotemporal evolutionary information in dynamic multi-objective optimisation

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CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2023年

作者： Fan, Qinqin Jiang, Min Huang, Wentao Jiang, Qingchao Shanghai Maritime Univ Logist Res Ctr Shanghai Peoples R China Xiamen Univ Sch Informat Xiamen Fujian Peoples R China Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Shanghai Peoples R China East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai Peoples R China 1550 Haigang Ave Shanghai 201306 Peoples R China

Preserving population diversity and providing knowledge, which are two core tasks in the dynamic multi-objective optimisation (DMO), are challenging since the sampling space is time- and space-varying. Therefore, the spatiotemporal property of evolutionary information needs to be considered in the DMO. In the present study, a sliding-time-window-based population clustering method (SPC) is proposed to effectively solve dynamic multi-objective optimisation problems (DMOPs). In the SPC, the knowledge is provided by saving historical data in the temporal dimension, and a spectral clustering method is used to divide the saved data into multiple neighbourhood subspaces for preserving population diversity in the spatial dimension. The SPC is incorporated into the RM-MEDA and is compared with other recently proposed state-of-the-art dynamic multi-objective evolutionary algorithms (DMOEAs) on 14 DMOPs introduced in IEEE CEC2018. Simulation results demonstrate that the proposed method is capable of enhancing the tracking performance of the RM-MEDA in the dynamically changing environments. Additionally, the SPC is utilised to solve an actual dynamic multi-objective translation control problem of an immersed tunnel element. Results show that the proposed SPC outperforms the knee point-based transfer learning method in terms of both computational cost and tracking performance.

关键词： dynamic multi-objective optimization dynamic optimization evolutionary computation high-performing computing immersed tunnel element

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A dynamic multi-objective evolutionary algorithm using a change severity-based adaptive population management strategy

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SOFT COMPUTING 2017年第4期21卷 885-906页

作者： Azzouz, Radhia Bechikh, Slim Ben Said, Lamjed Univ Tunis SOIE Lab Tunis Tunisia

In addition to the need for simultaneously optimizing several competing objectives, many real-world problems are also dynamic in nature. These problems are called dynamic multi-objective optimization problems. Applying evolutionary algorithms to solve dynamic optimization problems has obtained great attention among many researchers. However, most of works are restricted to the single-objective case. In this work, we propose an adaptive hybrid population management strategy using memory, local search and random strategies, to effectively handle environment dynamicity for the multi-objective case where objective functions change over time. Moreover, the proposed strategy is based on a new technique that detects the change severity, according to which it adjusts the number of memory and random solutions to be used. This ensures, on the one hand, a high level of convergence and on the other hand, the required diversity. We propose a dynamic version of the Non dominated Sorting Genetic Algorithm II, within which we integrate the above-mentioned strategies. Empirical results show that our proposal based on the use of the adaptive strategy is able to handle dynamic environments and to track the Pareto front as it changes over time. Moreover, when confronted with several recently proposed dynamic algorithms, it has presented competitive and better results on most problems.

关键词： Change severity sensing dynamic multi-objective optimization Time-changing objective functions Adaptive populationmanagement Memory-based strategy Local search-based strategy Change severity-based strategy

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Reproducibility and Baseline Reporting for dynamic multi-objective Benchmark Problems

Reproducibility and Baseline Reporting for Dynamic Multi-obj...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Herring, Daniel Kirley, Michael Yao, Xin Univ Melbourne Melbourne Vic Australia Univ Birmingham Birmingham W Midlands England

ISBN: (纸本)9781450392372

dynamic multi-objective optimization problems (DMOPs) are widely accepted to be more challenging than stationary problems due to the time-dependent nature of the objective functions and/or constraints. Evaluation of purpose-built algorithms for DMOPs is often performed on narrow selections of dynamic instances with differing change magnitude and frequency or a limited selection of problems. In this paper, we focus on the reproducibility of simulation experiments for parameters of DMOPs. Our framework is based on an extension of PlatEMO, allowing for the reproduction of results and performance measurements across a range of dynamic settings and problems. A baseline schema for dynamic algorithm evaluation is introduced, which provides a mechanism to interrogate performance and optimization behaviours of well-known evolutionary algorithms that were not designed specifically for DMOPs. Importantly, by determining the maximum capability of non-dynamic multi-objective evolutionary algorithms, we can establish the minimum capability required of purpose-built dynamic algorithms to be useful. The simplest modifications to manage dynamic changes introduce diversity. Allowing non-dynamic algorithms to incorporate mutated/random solutions after change events determines the improvement possible with minor algorithm modifications. Future expansion to include current dynamic algorithms will enable reproduction of their results and verification of their abilities and performance across DMOP benchmark space.

关键词： dynamic multi-objective optimization dynamic optimization dynamic benchmarks dynamic parameters dynamic frequency and severity

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