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Evolutionary Strategy to Enhance an RNA Design Tool Performance

IEEE ACCESS 2024年 12卷 15582-15593页

作者： Rubio-Largo, Alvaro Escobar-Encinas, Laura Lozano-Garcia, Nuria Granado-Criado, Jose Maria Univ Extremadura Escuela Politecn Dept Comp & Telemat Syst Engn Caceres 10003 Spain Univ Extremadura Escuela Politecn Dept Comp & Commun Technol Caceres 10003 Spain

At present, designing an RNA sequence that folds into a specific secondary structure is a problem that is not fully solved, due to its exponentially increasing complexity. To address this matter, many computational methods have been developed, but none of them has been able to completely and in an affordable time solve Eterna100, a widely recognized benchmark used to test the performance of RNA inverse folding algorithms. In previous publications we presented the m2dRNAs tool, a multiobjective Evolutionary Algorithm, and its extension eM2dRNAs, which added a recursive decomposition of the target structure, thus simplifying the problem. At that time they successfully improved the ability to solve the RNA inverse folding problem, but were still unable to complete the Eterna100 benchmark. Here we introduce ES+eM2dRNAs, an improvement of eM2dRNAs that optimizes the decomposition process, as a drawback in its nature was identified.A comparative study of this new tool against its predecessors and other RNA design methods was performed using the two current versions of the Eterna100 benchmark. ES+eM2dRNAs was shown to be the best in all performance indicators considered (number of structures solved, success rate, and total run time). Moreover, it is able to solve two Eterna100 structures for which none of the compared methods had ever found a solution.

关键词： Bioinformatics Sequential analysis evolutionary strategy genetic algorithm multiobjective evolutionary algorithm multiobjective optimization problem ncRNA design RNA inverse folding RNA secondary structure decomposition strategy

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Optimal scheduling of block lifting in consideration of the minimization of traveling distance while unloaded and wire and shackle replacement of a gantry crane

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JOURNAL OF MARINE SCIENCE AND TECHNOLOGY 2010年第2期15卷 190-200页

作者： Roh, Myung-Il Lee, Kyu-Yeul Univ Ulsan Sch Naval Architecture & Ocean Engn Ulsan 680749 South Korea Seoul Natl Univ Dept Naval Architecture & Ocean Engn Res Inst Marine Syst Engn Seoul 151742 South Korea

Nowadays, shipyards are making every effort to efficiently manage their resources such as gantry cranes, transporters, and block stock yards. The scheduling of block lifting for a gantry crane has been manually performed by an expert shipyard manager for many years. Such a practice, however, can lead to an undesirably long time to produce scheduling plans. In addition, the quality of the scheduling plans may not be optimal. To improve the overall process, a block lifting scheduling system for a gantry crane was developed in this study by using optimization techniques. A block lifting scheduling problem was first formulated as a multiobjective optimization problem. For a gantry crane, minimization of the traveling distance while unloaded and wire and shackle replacement between block lifting was considered. An optimization algorithm based on the genetic algorithm was then proposed and implemented so as to solve the problem. To evaluate the efficiency and applicability of the developed system, the system was applied to an actual block lifting scheduling problem in a shipyard. Compared to results achieved by manual scheduling by an expert manager, the results of this work show that blocks can be more efficiently lifted by a gantry crane when the developed system is applied.

关键词： Lock lifting scheduling Gantry crane Genetic algorithm multiobjective optimization problem Production planning Shipbuilding

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FLEXIBLE GENERATION MIX UNDER multiobjectiveS AND UNCERTAINTIES

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IEEE TRANSACTIONS ON POWER SYSTEMS 1993年第2期8卷 581-587页

作者： TANABE, R YASUDA, K YOKOYAMA, R SASAKI, H HIROSHIMA UNIV DEPT ELECT ENGNHIGASHIHIROSHIMA 724JAPAN

This paper presents criteria for evaluating flexibility of a generation mix and develops an efficient computational algorithm for determining a flexible generation mix considering uncertainties in the power system expansion planning. The flexible generation mix problem is formulated as a multiobjective optimization problem and solved by the dynamic programming technique in which each type of generation plants is selected as a stage and generation capacity is selected as a state. The proposed method can treat easily not only the uncertainties but also many constraints of generation planning such as integer solutions of generation capacities, reliability constraints, and so on. The effectiveness and feasibility of the proposed method are demonstrated on a typical power system model.

关键词： GENERATION EXPANSION PLANNING UNCERTAINTIES FLEXIBLE GENERATION MIX GENERATION MIX FLEXIBILITY multiobjective optimization problem DYNAMIC PROGRAMMING

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Seeking global edges for traveling salesman problem in multi-start search

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JOURNAL OF GLOBAL optimization 2011年第3期51卷 515-540页

作者： Li, Weiqi Univ Michigan Sch Management Flint MI 48501 USA

This study investigates the properties of the edges in a set of locally optimal tours found by multi-start search algorithm for the traveling salesman problem (TSP). A matrix data structure is used to collect global information about edges from the set of locally optimal tours and to identify globally superior edges for the problem. The properties of these edges are analyzed. Based on these globally superior edges, a solution attractor is formed in the data matrix. The solution attractor is a small region of the solution space, which contains the most promising solutions. Then an exhausted enumeration process searches the solution attractor and outputs all solutions in the attractor, including the globally optimal solution. Using this strategy, this study develops a procedure to tackler a multi-objective TSP. This procedure not only generates a set of Pareto-optimal solutions, but also be able to provide the structural information about each of the solutions that will allow a decision-maker to choose the best compromise solution.

关键词： Multi-start search Traveling salesman problem Convergence Solution attractor multiobjective optimization problem Pareto optimal solution

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Data-Driven multiobjective optimization for Burden Surface in Blast Furnace With Feedback Compensation

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第4期16卷 2233-2244页

作者： Li, Yanjiao Zhang, Sen Zhang, Jie Yin, Yixin Xiao, Wendong Zhang, Zhiqiang Univ Sci & Technol Beijing Sch Automat & Engn Beijing 100083 Peoples R China Univ Leeds Sch Elect & Elect Engn Leeds LS2 9JT W Yorkshire England

In this paper, an intelligent data-driven optimization scheme is proposed for finding the proper burden surface distribution, which exerts large influences on keeping blast furnace running smoothly in an energy-efficient state. In the proposed scheme, production indicators prediction models are first developed using a kernel extreme learning machine algorithm. To heel, burden surface decision is presented as a multiobjective optimization problem for the first time and solved by a modified two-stage intelligent optimization strategy to generate the initial setting values of burden surface. Furthermore, considering the existence of the approximation error of the created prediction models, feedback compensation is implemented to enhance the reliability of the results, in which an improved association rule mining method is developed to find the corrected values to compensate the initial setting values. Finally, we apply the proposed optimization scheme to determine the setting values of burden surface using actual data, and experimental results illustrate its effectiveness and feasibility.

关键词： Production Surface treatment optimization Surface charging Iron Furnaces Informatics Blast furnace (BF) burden surface feedback compensation kernel extreme learning machine (KELM) multiobjective optimization problem

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Game theory based coevolutionary algorithm: A new computational coevolutionary approach

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2004年第4期2卷 463-474页

作者： Sim, KB Lee, DW Kim, JY Chung Ang Univ Sch Elect & Elect Engn Seoul 156756 South Korea Univ Tennessee Dept Elect & Comp Engn Knoxville TN 37996 USA

Game theory is a method of mathematical analysis developed to study the decision making process. In 1928, Von Neumann mathematically proved that every two-person, zerosum game with many pure finite strategies for each player is deterministic. In the early 50's, Nash presented another concept as the basis for a generalization of Von Neumann's theorem. Another central achievement of game theory is the introduction of evolutionary game theory, by which agents can play optimal strategies in the absence of rationality. Through the process of Darwinian selection, a population of agents can evolve to an Evolutionary Stable Strategy (ESS) as introduced by Maynard Smith in 1982. Keeping pace with these game theoretical studies, the first computer simulation of coevolution was tried out by Hillis. Moreover, Kauffman proposed the NK model to analyze coevolutionary dynamics between different species. He showed how coevolutionary phenomenon reaches static states and that these states are either Nash equilibrium or ESS in game theory. Since studies concerning coevolutionary phenomenon were initiated, there have been numerous other researchers who have developed coevolutionary algorithms. In this paper we propose a new coevolutionary algorithm named Game theory based Coevolutionary Algorithm (GCEA) and we confirm that this algorithm can be a solution of evolutionary problems by searching the ESS. To evaluate this newly designed approach, we solve several test multiobjective optimization problems (MOPs). From the results of these evaluations, we confirm that evolutionary game can be embodied by the coevolutionary algorithm and analyze the optimization performance of our algorithm by comparing the performance of our algorithm with that of other evolutionary optimization algorithms.

关键词： coevolutionary algorithm evolutionary stable strategy game theory multiobjective optimization problem

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multiobjective big data optimization based on a hybrid salp swarm algorithm and differential evolution

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APPLIED MATHEMATICAL MODELLING 2020年 80卷 929-943页

作者： Abd Elaziz, Mohamed Li, Lin Jayasena, K. P. N. Xiong, Shengwu Wuhan Univ Technol Sch Comp Sci & Technol Wuhan Peoples R China Sabaragamuwa Univ Sri Lanka Dept Comp & Informat Syst Belihuloya Sri Lanka Zagazig Univ Fac Sci Dept Math Zagazig Egypt

This paper developed a multiobjective Big Data optimization approach based on a hybrid salp swarm algorithm and the differential evolution algorithm. The role of the differential evolution algorithm is to enhance the capability of the feature exploitation of the salp swarm algorithm because the operators of the differential evolution algorithm are used as local search operators. In general, the proposed method contains three stages. In the first stage, the population is generated, and the archive is initialized. The second stage updates the solutions using the hybrid salp swarm algorithm and the differential evolution algorithm, and the final stage determines the nondominated solutions and updates the archive. To assess the performance of the proposed approach, a series of experiments were performed. A set of single-objective and multiobjective problems from the 2015 Big Data optimization competition were tested;the dataset contained data with and without noise. The results of our experiments illustrated that the proposed approach outperformed other approaches, including the baseline nondominated sorting genetic algorithm, on all test problems. Moreover, for single-objective problems, the score value of the proposed method was better than that of the traditional multiobjective salp swarm algorithm. When compared with both algorithms, that is, the adaptive DE algorithm with external archive and the hybrid multiobjective firefly algorithm, its score was the largest. In contrast, for the multiobjective functions, the scores of the proposed algorithm were higher than that of the fireworks algorithm framework. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Big Data optimization problem multiobjective optimization problem Metaheuristic techniques Salp swarm algorithm Differential evolution

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The crowd framework for multiobjective particle swarm optimization

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ARTIFICIAL INTELLIGENCE REVIEW 2014年第4期42卷 1095-1138页

作者： Xu, Heming Wang, Yinglin Xu, Xin Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

multiobjective particle swarm optimization meets two difficulties-guiding the search towards the Pareto front and maintaining diversity of the obtained solutions-so a great number of improvements are possible. Our crowd framework systematically summarizes these improvements, extracts them into reusable strategies and categorizes them into modules by their optimization mechanisms. We introduce a number of new techniques within the modules. Strategies are compared first theoretically and then practically through amended ZDT series. We propose a sequence for module application based on the correlation between the modules. The resulting algorithms give incredible performance. Thus our crowd framework forms a new baseline for MOPSO.

关键词： Crowd framework multiobjective optimization problem Particle swarm optimization ZDT series

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Joint optimization of Latency and Energy Consumption via Deep Reinforcement Learning for Proximity Detection in Road Networks

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第12期25卷 19457-19468页

作者： Liu, Yaqiong Zhao, Tongyu Shou, Guochu Zhang, Yan Beijing Univ Posts & Telecommun Beijing Lab Adv Informat Networks Beijing Key Lab Network Syst Architecture & Conve Sch Informat & Commun Engn Beijing 100876 Peoples R China Univ Oslo Dept Informat N-0316 Oslo Norway Univ Oslo Simula Metropolitan Ctr Digital Engn N-0316 Oslo Norway

The development of automatic driving and assisted driving breeds the problem of proximity detection in road networks, which plays a significant role in ensuring safe driving. Due to the fact that it is a time-sensitive task, the problem of proximity detection requires to judge whether two vehicles are close to each other in a very short time. However, the battery life and computation capacity of vehicles are limited in the actual scenario. Therefore, how to solve this problem with low latency and energy consumption is an important issue. In this paper, we investigate the Joint optimization of the Latency and Energy consumption problem in the scenario of Proximity Detection, namely, JOLE-PD, which is formulated into a constrained multiobjective optimization problem. The DDPG-CMOA method is proposed to find a tradeoff between latency and energy consumption, achieving the Pareto optimal solutions. Besides, NSGA-II (Non-dominated Sorting Genetic Algorithm-II) and MOEA-D (Multi-objective Evolutionary Algorithm Based on Decomposition), as the typical algorithms to solve multiobjective optimization problem, are used as the baseline methods to compare the performance of DDPG-CMOA method under different parameters. The experimental results show the proposed DDPG-CMOA method requires much lower running time and has strong generalization ability. Moreover, the solutions obtained from the DDPG-CMOA method have a slightly better ability of convergence and diversity.

关键词： Deep reinforcement learning (DRL) multiobjective optimization problem proximity detection vehicular edge computing (VEC) edge intelligence Internet of Vehicles (IoV)

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Efficient control and multi-criteria energy scheduling of renewable-based utility grid via pareto-metaheuristic optimizers

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IET RENEWABLE POWER GENERATION 2022年第6期16卷 1246-1266页

作者： Ramadan, Haitham S. Iqbal, Mehroze Becherif, Mohamed Alhelou, Hassan Haes Inst Int Sur Stockage Hydrogene ISTHY Meroux Moval France Zagazig Univ Fac Engn Elect Power & Machines Dept Zagazig Egypt Univ Bourgogne Franche Comte UTBM FCLAB FEMTO ST InstCNRS Belfort France Tishreen Univ Dept Elect Power Engn Latakia Syria

Being a renewable influenced utility grid, the wind integrated hydrothermal system (WHTS) is considered as a future-proof and contemporary energy system to reduce the dependency on depleting fossil fuels. However, due to intermittent nature of wind energy and complex operational constraints, offering the day-ahead operation schedule of WHTS with joint awareness of fuel economy and emissions is classified as a complicated multi-objective optimization problem. The major challenge is to maximize the utilization of clean hydro power to accommodate uncertain wind energy together with reduced dependency on fossils based thermal power. In this context, the paper devises an efficient control and multi-criteria energy scheduling method, essentially comprised of adaptive repair and volume restriction to maximize the utilization of available hydro power. The pareto-metaheuristic optimizer based on non-dominated sorting is exploited for the day-ahead scheduling of WHTS based utility grid. The most appropriate solution from pareto-optimal archive is selected using fuzzy-ranking index. The expected reductions in fuel cost and emissions are reflected using a significant probability scheme. The effectiveness of the proposed approach based on pareto-metaheuristic is appraised by various case studies. The simulation results provide a suitable day-ahead generation schedule of WHTS based grid, considering both economics and environmental aspects.

关键词： contemporary energy system power generation control depleting fossil fuels wind energy adaptive repair day-ahead generation schedule wind integrated hydrothermal system fossil fuels power generation economics maintenance engineering fuel economy-emissions Pareto optimisation fuzzy-ranking index renewable-based utility grid hydrothermal power systems metaheuristics economics-environmental aspects power grids WHTS based utility grid fossils based thermal power pareto-metaheuristic optimizer hydro power probability multiobjective optimization problem fuel economy multicriteria energy scheduling method probability scheme wind power fuel cost power generation scheduling

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