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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Wu, Sheng-Hao Du, Ke-Jing Zhan, Zhi-Hui Wang, Hua Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Victoria Univ Inst Sustainable Ind & Liveable Cities Melbourne Vic 8001 Australia

ISBN: (纸本)9781728183923

dynamic optimization problems (DOP) widely exist in many application fields and remain a challenge. The multi-population evolutionary computation approach is an efficient framework for solving the DOPs. The key issue of the multi-population approach is how to efficiently generate subpopulations in new environment by well reusing historical information in past environments. However, most existing works try to divide the population into small ones merely considering the individuals' distribution, which may have the limitation that the partition leads to the unbalanced computational resources allocation among subpopulations. To address this limitation, we propose a historical information-based differential evolution (HIDE) to effectively solve the DOP. Firstly, a region-based subpopulation initialization (RSI) strategy is proposed to generate multiple subpopulations in the new environment in a balanced way. By initializing multiple subpopulations in different regions of the search space, the diversity of the population is enhanced, which is helpful to solve the DOP with multiple peaks. Secondly, to fully make use of the found peaks of the previous environments, an archive-based historical information reuse (AHIR) strategy is put forward to manage and reuse the historical information to guide the search in the new environment. Experiments have been carried out on the moving peaks benchmark (MPB), which is a commonly used DOP test suite. Experimental results show that the proposed HIDE algorithm generally outperforms the classic DE algorithms with different parameter settings and some existing competitive state-of-the-art DOP approaches.

关键词： dynamic optimization problem differential evolution multi-population approach

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Diversity-driven Multi-population Particle Swarm optimization for dynamic optimization problem

Diversity-driven Multi-population Particle Swarm Optimizatio...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Zhu, Pei-Yao Wu, Sheng-Hao Du, Ke-Jing Wang, Hua Zhang, Jun Zhan, Zhi-Hui South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Victoria Univ Melbourne Vic Australia Hangyang Univ Ansan 15588 South Korea

ISBN: (纸本)9798400701207

dynamic optimization problem (DOP) is a kind of problem that contains a series of static problems with different problem characteristics. The main idea of the existing dynamic optimization algorithms is to continuously locate and track changing optimal solutions using limited computational resources. Hence, how to strengthen the exploration ability for locating the optimum of the static problem in an environment and how to improve the adaptation ability to the changing optima in different environments are two key issues for efficiently solving DOP. To address these issues, we propose a diversity-driven multi-population particle swarm optimization (DMPSO) algorithm. First, we propose a center information based update strategy to strengthen the exploration ability of the PSO algorithm in each subpopulation. Second, a stagnant subpopulation activation strategy is proposed to activate the stagnant subpopulations, and a random walk strategy is proposed to improve the optima tracking capability of the best-performing subpopulation. Third, an archive-based initialization strategy is proposed to reinitialize the population. Experimental studies are conducted on the moving peaks benchmark to compare the DMPSO algorithm with some state-of-the-art dynamic optimization algorithms. The experimental results show that the proposed DMPSO algorithm outperforms the contender algorithms which validate the effectiveness of the proposed algorithm.

关键词： dynamic optimization problem diversity strategy multi-population framework particle swarm optimization

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The analysis of the dynamic optimization problem in econophysics from the point of view of the symplectic approach for constrained systems

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CHAOS SOLITONS & FRACTALS 2021年 145卷 110752-110752页

作者： Godinho, Cresus F. L. Abreu, Everton M. C. Univ Fed Rural Rio De Janeiro Dept Fis BR-23890971 Seropedica RJ Brazil Univ Fed Juiz De Fora Dept Fis BR-36036330 Juiz De Fora MG Brazil Univ Fed Rio de Janeiro Programa Posgrad Interdisciplinar Fis Aplic Inst Fis BR-21941972 Rio De Janeiro RJ Brazil

A standard approach to deal with the problems relative to dynamic optimization is the well known Pontryagin method to obtain a concise condition for an optimal control, minimizing the cost functional. In this paper we have proposed a simpler dynamic optimization procedure through a so-called symplectic algorithm. We worked with the analogy between the physical systems at the classical and quantum energy levels. In this way, we started by considering some cost functional f(q, u, t), where q and u are state and control variables, respectively. We have shown that it is possible to investigate the system by means of a symplectic extension, where we can reduce any constrained system into its canonical first order form. Consequently, the dynamics of evolution, usually governed by Dirac's constraint method, was re-obtained here in a very elegant way. On the other hand, the constraints classification turned out to be different from the one used in Dirac's procedure. Of course, our analysis is valid for unconstrained systems where the Pontryagin equations are valid too. Four optimization problems were solved. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Econophysics dynamic optimization problem Constrained systems

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A memetic ant colony optimization algorithm for the dynamic travelling salesman problem

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SOFT COMPUTING 2011年第7期15卷 1405-1425页

作者： Mavrovouniotis, Michalis Yang, Shengxiang Univ Leicester Dept Comp Sci Leicester LE1 7RH Leics England Brunel Univ Dept Informat Syst & Comp Uxbridge UB8 3PH Middx England

Ant colony optimization (ACO) has been successfully applied for combinatorial optimization problems, e.g., the travelling salesman problem (TSP), under stationary environments. In this paper, we consider the dynamic TSP (DTSP), where cities are replaced by new ones during the execution of the algorithm. Under such environments, traditional ACO algorithms face a serious challenge: once they converge, they cannot adapt efficiently to environmental changes. To improve the performance of ACO on the DTSP, we investigate a hybridized ACO with local search (LS), called Memetic ACO (M-ACO) algorithm, which is based on the population-based ACO (P-ACO) framework and an adaptive inver-over operator, to solve the DTSP. Moreover, to address premature convergence, we introduce random immigrants to the population of M-ACO when identical ants are stored. The simulation experiments on a series of dynamic environments generated from a set of benchmark TSP instances show that LS is beneficial for ACO algorithms when applied on the DTSP, since it achieves better performance than other traditional ACO and P-ACO algorithms.

关键词： Memetic algorithm Ant colony optimization dynamic optimization problem Travelling salesman problem Inver-over operator Local search Simple inversion Adaptive inversion

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Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors

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APPLIED SOFT COMPUTING 2013年第10期13卷 4023-4037页

作者： Mavrovouniotis, Michalis Yang, Shengxiang De Montfort Univ Sch Comp Sci & Informat CCI Leicester LE1 9BH Leics England

Traditional ant colony optimization (ACO) algorithms have difficulty in addressing dynamic optimization problems (DOPs). This is because once the algorithm converges to a solution and a dynamic change occurs, it is difficult for the population to adapt to a new environment since high levels of pheromone will be generated to a single trail and force the ants to follow it even after a dynamic change. A good solution to address this problem is to increase the diversity via transferring knowledge from previous environments to the pheromone trails using immigrants schemes. In this paper, an ACO framework for dynamic environments is proposed where different immigrants schemes, including random immigrants, elitism-based immigrants, and memory-based immigrants, are integrated into ACO algorithms for solving DOPs. From this framework, three ACO algorithms, where immigrant ants are generated using the aforementioned immigrants schemes and replace existing ants in the current population, are proposed and investigated. Moreover, two novel types of dynamic travelling salesman problems (DTSPs) with traffic factors, i. e., under random and cyclic dynamic environments, are proposed for the experimental study. The experimental results based on different DTSP test cases show that each proposed algorithm performs well on different environmental cases and that the proposed algorithms outperform several other peer ACO algorithms. (C) 2013 Elsevier B. V. All rights reserved.

关键词： Ant colony optimization Immigrants schemes dynamic optimization problem dynamic travelling salesman problem Traffic factor

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Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays

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SOFT COMPUTING 2016年第8期20卷 2951-2966页

作者： Eaton, Jayne Yang, Shengxiang Mavrovouniotis, Michalis De Montfort Univ Sch Comp Sci & Informat CCI Leicester LE1 9BH Leics England

Train rescheduling after a perturbation is a challenging task and is an important concern of the railway industry as delayed trains can lead to large fines, disgruntled customers and loss of revenue. Sometimes not just one delay but several unrelated delays can occur in a short space of time which makes the problem even more challenging. In addition, the problem is a dynamic one that changes over time for, as trains are waiting to be rescheduled at the junction, more timetabled trains will be arriving, which will change the nature of the problem. The aim of this research is to investigate the application of several different ant colony optimization (ACO) algorithms to the problem of a dynamic train delay scenario with multiple delays. The algorithms not only resequence the trains at the junction but also resequence the trains at the stations, which is considered to be a first step towards expanding the problem to consider a larger area of the railway network. The results show that, in this dynamic rescheduling problem, ACO algorithms with a memory cope with dynamic changes better than an ACO algorithm that uses only pheromone evaporation to remove redundant pheromone trails. In addition, it has been shown that if the ant solutions in memory become irreparably infeasible it is possible to replace them with elite immigrants, based on the best-so-far ant, and still obtain a good performance.

关键词： dynamic railway junction rescheduling Ant colony optimization UK railway network Rail transportation dynamic optimization problem

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Optimal control for the metabolic activity of microbial cells through the metabolic flux parameterization

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2025年

作者： Li, Quan Jin, Liqiang Tao, Chenxi Zhao, Zhonggai Liu, Fei Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Inst Automat Wuxi Peoples R China Changzhou Univ Sch Microelect & Control Engn Changzhou Peoples R China Southeast Univ Sch Automat Nanjing Peoples R China

During microbial fermentation, the metabolic fluxes reflect the growth and reproduction rate of microbial cells. By adjusting the metabolic fluxes, the desired production target can be achieved. It is highly necessary to ensure that the fermentation process meets the production constraints because the metabolic fluxes are regulated by various enzymatic reactions. However, previous studies cannot guarantee that the production constraints are satisfied over the course of the process. In this paper, constraints are designed at the microscopic flux level for the E. coli cell fermentation process. A control variable parameterization method, which discretizes the control variables while the state variables remain continuous, is used to optimize the E. coli cell growth activity, where the time domain is divided into several subintervals. In each subinterval, the metabolic fluxes are approximated by a series of parameters to be optimized. Then, the E. coli metabolic activity is transformed into a dynamic optimization problem, and the optimal trajectories of metabolic fluxes are obtained by solving the problem. Finally, the simulation results of the E. coli fermentation process verify the effectiveness of the method.

关键词： control variable parameterization (CVP) dynamic optimization problem E. coli cells metabolic flux product constraint conditions

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Ensemble framework by using nature inspired algorithms for the early-stage forest fire rescue-A case study of dynamic optimization problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年第0期90卷 103517-000页

作者： Zhang, HongGuang Liang, ZiHan Liu, HuaJian Wang, Rui Liu, YuanAn Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Key Lab Work Safety Intelligent Monitorin Beijing Peoples R China

In this paper, we propose rescue ensemble to simulate the dynamic rescue process between forest fire spread and forest fire rescue, while simultaneously formulating this process as a dynamic optimization problem. However, there is still little research about simulating this kind of the dynamic rescue process, even when many new unmanned monitoring systems and large-scale firefighting aircraft emerge in the forest-fire-rescue field. Our rescue ensemble that consists of rescue simulator and rescue algorithm is characterized by supporting the offline simulation of the dynamic rescue process between forest fire spread (like offensive forces) and forest fire rescue (like defensive forces). Based on modifying the cellular automaton model of forest fire spread, rescue simulator is able to simulate forest fire spread and aircraft firefighting, simultaneously. Besides, the main goal of rescue algorithm is to realize the aircraft task allocation. Firefighting particle swarm optimization is proposed by us as our rescue algorithm, which is characterized by considering fire edge suppression, the burning-cell continuity, and wind direction. We construct our test problems based on real forest maps and aircraft firefighting capability. Comparing with four compared rescue algorithms, we test the different capabilities of firefighting particle swarm optimization, such as searching dynamic optimal solution, shortening the rescue time, controlling the spread speed of fire edge, and minimizing the burned cost. Experimental results demonstrate that the framework of rescue ensemble is feasible. Meanwhile, the results of firefighting particle swarm optimization are satisfactory in most cases.

关键词： Forest fire suppression Rescue ensemble Forest fire rescue Rescue simulator dynamic optimization problem Unmanned platforms

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Multi-objective differential evolution with performance-metric-based self-adaptive mutation operator for chemical and qbiochemical dynamic optimization problems

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APPLIED SOFT COMPUTING 2017年 59卷 33-44页

作者： Fan, Qinqin Wang, Weili Yan, Xuefeng Shanghai Maritime Univ Logistics Res Ctr Shanghai 201306 Peoples R China East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

Each mutation operator of differential evolution (DE) algorithm is generally suitable for certain specific types of multi-objective optimization problems (MOPs) or particular stages of the evolution. To automatically select an appropriate mutation operator for solving MOPs in different phases of the evolution, a multi-objective differential evolution with performance-metric-based self-adaptive mutation operator (MODE-PMSMO) is proposed in this study. In MODE-PMSMO, a modified inverted generational distance (IGD) is utilized to evaluate the performance of each mutation operator and guide the evolution of mutation operators. The proposed MODE-PMSMO is then compared with seven multi-objective evolutionary algorithms (MOEAs) on five bi-objective and five tri-objective optimization problems. Generally, MODE-PMSMO exhibits the best average performance among all compared algorithms on ten MOPs. Additionally, MODE-PMSMO is employed to solve four typical multi-objective dynamic optimization problems in chemical and biochemical processes. Experimental results indicate that MODE-PMSMO is suitable for solving these actual problems and can provide a set of nondominated solutions for references of decision makers. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Chemical and biochemical processes Differential evolution algorithm dynamic optimization problem Multi-objective optimization

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A note on the paper "A multi-population harmony search algorithm with external archive for dynamic optimization problems" by Turky and Abdullah

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INFORMATION SCIENCES 2014年第1期288卷 12-14页

作者： Ranginkaman, Amir Ehsan Kordestani, Javidari Kazemi Rezyanian, Alireza Meybodi, Mohammad Reza Islamic Azad Univ Dept Elect Comp & IT Engn Qazvin Branch Tehran Iran Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Amirkabir Univ Technol Tehran Polytech Comp Engn & Informat Technol Dept Soft Comp Lab Tehran Iran

In a very recently presented paper, Turky and Abdullah [5] proposed a novel multi-population harmony search with external archive (MHSA-ExtArchive) for dynamic optimization problems. In the experimental results, the authors claimed that their approach could outperform several state-of-the-art algorithms. They also showed the superiority of their method by means of numerical experiments on Moving Peaks Benchmark (MPB). Despite the interesting idea of applying multi-population scheme on harmony search and using a new type of external archive for dealing with dynamic problems, we believe that there are two very important shortcomings in the result analysis, which we point out in this short note. The main motivation of the present note is to contribute toward preventing the same mistakes from happening by the other researchers. (C) 2014 Elsevier Inc. All rights reserved.

关键词： dynamic optimization problem dynamic environment Multi-population harmony search Harmony search with external archive Moving Peaks Benchmark (MPB)

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