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An Improved coevolutionary algorithm for Constrained Multi-Objective Optimization Problems

INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2024年第1期18卷

作者： Xie, Shumin Zhu, Zhenjia Wang, Hui Shenzhen Inst Informat Technol Shenzhen Peoples R China

Constrained multi-objective optimization problems are ubiquitous in engineering applications. In recent years, constrained multi-objective optimization algorithms based on the dual population coevolutionary framework have been widely studied due to their excellent performance. However, when facing optimization problems with complex constraints, the performance of existing algorithms still needs further improvement. This paper proposes an improved constrained multi-objective coevolutionary algorithm (iCMOCA). The algorithm mainly includes two populations: One population takes into account constraints, while the other population disregards them. Meanwhile, the iCMOCA employs effective collaboration between two populations during the process of offspring generation and environmental selection, and it utilizes an environmental selection strategy based on multi-objective to multi-objective decomposition to improve the performance. Comparative analysis conducted on the DAS-CMOP and MW test suites provides empirical evidence that iCMOCA outperforms five state-of-the-art algorithms.

关键词： Constrained Multi-Objective Optimization Dual-Population coevolutionary algorithm

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Optimal resource allocation for transmission diversity in multi-radio access networks:a coevolutionary genetic algorithm approach

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Science China(Information Sciences) 2014年第2期57卷 99-112页

作者： FAN WenHao LIU YuanAn WU Fan Beijing Key Laboratory of Work Safety Intelligent Monitoring Beijing University of Posts and Telecommunications Wireless Communications and EMC Laboratory School of Electronic EngineeringBeijing University of Posts and Telecommunications

The next generation wireless communication systems aim at supporting enhanced diversified network access and data transmission abilities via the cooperative integration and unified management of various radio access technologies(RATs).The resource allocation is the core component leading the network system and mobile terminals to the service robustness and performance *** this paper,a numeric optimization model for optimizing terminals’transmission power and allocated RAT bandwidth for maximizing system capacity is proposed with the focal consideration of the multi-radio transmission diversity for parallel transmission through multiple links from diferent RATs,and diferent terminal characteristics on RAT ***,we design a centralized and periodic scheduling algorithm including an improved coevolutionary genetic algorithm for efciently solving the optimization *** results demonstrate that our propose algorithm can distinctly enhance the system performance and improve the computational efciency.

关键词： multi-radio access radio resource management multi-radio transmission diversity Lagrangian method coevolutionary algorithm genetic algorithm

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Solving dual problems using a coevolutionary optimization algorithm

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JOURNAL OF GLOBAL OPTIMIZATION 2013年第3期57卷 891-933页

作者： Deb, Kalyanmoy Gupta, Shivam Dutta, Joydeep Ranjan, Bhoomija Indian Inst Technol Dept Mech Engn Kanpur 208016 Uttar Pradesh India Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Aalto Univ Dept Informat & Serv Econ Sch Econ Helsinki 00100 Finland Univ Texas Dallas Naveen Jindal Sch Management Richardson TX 75080 USA Indian Inst Technol Dept Math & Stat Kanpur 208016 Uttar Pradesh India Univ Rochester Simon Sch Business Rochester NY USA

In solving certain optimization problems, the corresponding Lagrangian dual problem is often solved simply because in these problems the dual problem is easier to solve than the original primal problem. Another reason for their solution is the implication of the weak duality theorem which suggests that under certain conditions the optimal dual function value is smaller than or equal to the optimal primal objective value. The dual problem is a special case of a bilevel programming problem involving Lagrange multipliers as upper-level variables and decision variables as lower-level variables. Another interesting aspect of dual problems is that both lower and upper-level optimization problems involve only box constraints and no other equality of inequality constraints. In this paper, we propose a coevolutionary dual optimization (CEDO) algorithm for co-evolving two populations-one involving Lagrange multipliers and other involving decision variables-to find the dual solution. On 11 test problems taken from the optimization literature, we demonstrate the efficacy of CEDO algorithm by comparing it with a couple of nested smooth and nonsmooth algorithms and a couple of previously suggested coevolutionary algorithms. The performance of CEDO algorithm is also compared with two classical methods involving nonsmooth (bundle) optimization methods. As a by-product, we analyze the test problems to find their associated duality gap and classify them into three categories having zero, finite or infinite duality gaps. The development of a coevolutionary approach, revealing the presence or absence of duality gap in a number of commonly-used test problems, and efficacy of the proposed coevolutionary algorithm compared to usual nested smooth and nonsmooth algorithms and other existing coevolutionary approaches remain as the hallmark of the current study.

关键词： Dual problem Duality gap Optimization Evolutionary algorithms Nonsmooth optimization algorithms coevolutionary algorithm

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A Formation Reconfiguration algorithm for Multi-UAVs Based on Distributed Cooperative coevolutionary with an Adaptive Grouping Strategy

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Chinese Journal of Electronics 2020年第5期29卷 841-851页

作者： LIU Huaxian LIU Feng ZHANG Xuejun GUAN Xiangmin CHEN Jun School of Electronic and Information Engineering Beihang University General Aviation Institute of Zhejiang JianDe Department of General Aviation Civil Aviation Management Institute of China School of Engineering and Materials Science Queen Mary University of London

The Formation reconfiguration problem plays a crucial role in the implementation of complex tasks for multiple unmanned aerial vehicles, which attracted increasing attention in the past decade. Taking into consideration the control parameters and time discretization of the multi-UAVs in the 3 Dimensional(3-D) space, the formation reconfiguration problem can be formulated as a large-scale combinatorial optimization problem with complex constraints and tight couplings between variables. The problem results in the reduction in efficiency and effectiveness using classic bio-inspired algorithms. In this paper, a formation reconfiguration method based on cooperative coevolutionary algorithm is proposed along with a new decomposition strategy to improve the optimization capability and prevent premature convergence. In the proposed approach, variables of multi-UAV are divided into several sub-groups based on an adaptive grouping strategy. The proposed strategy groups the variables in order to better deal with the tight coupling among them, taking into account the variables’ variance and multi-UAVs characteristics of the formation reconfiguration problem. Therefore, each subgroup can adopt the Self-adaptive differential evolution strategy with neighborhood search(SaNSDE) with the aim to optimize the UAV’s control inputs using multithreaded programming. SaNSDE contributes to calculating the results in a fully distributed and paralleled *** solution is then obtained through cooperation and coordination with all subcomponents. Simulation results based on extreme scenarios adopted by previous researches demonstrate that the proposed algorithm outperformed the existing approaches including Particle swarm optimization(PSO), Differential evolution(DE),and the cooperative coevolution algorithms with different well-known grouping strategies.

关键词： coevolutionary algorithm formation reconfiguration problem self-adaptive differential evolution strategy evolutionary computation multiUAV Grouping strategy particle swarm optimisation Cooperative coevolutionary decomposition strategy position control Formation reconfiguration Distributed differential evolution particle swarm optimization Multi-UAV adaptive grouping strategy large-scale combinatorial optimization problem cooperative coevolution algorithm formation reconfiguration algorithm autonomous aerial vehicles multiple unmanned aerial vehicles formation reconfiguration method search problems

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Equilibrium Calculation in Electricity Market Modeled as a Two-stage Stochastic Game using competitive coevolutionary algorithms

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IFAC Proceedings Volumes 2012年第21期45卷 524-529页

作者： Ahmed Amine Ladjici Ahmed Tiguercha Mohamed Boudour University of Sciences and Technology Houari-Boumediene Algiers Algeria Laboratory of Industrial and Electrical Systems (LSEI)

The present paper investigates the use of competitive coevolutionary algorithm in order to find supplier's optimal strategies in a deregulated electricity market, by calculating the Nash equilibrium strategies. In this work, we consider a stochastic model where spot market parameters are not known in advance. the market agents can take part in the forward and spot transactions and act strategically in order to increase their profits from both transactions.

关键词： Deregulated Electricity Market Market Equilibrium Two Stage Stochastic Game coevolutionary algorithm

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A cooperative coevolutionary approach dealing with the skull-face overlay uncertainty in forensic identification by craniofacial superimposition

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SOFT COMPUTING 2012年第5期16卷 797-808页

作者： Ibanez, O. Cordon, O. Damas, S. European Ctr Soft Comp Mieres 33600 Asturias Spain Univ Granada DECSAI E-18071 Granada Spain Univ Granada CITIC UGR E-18071 Granada Spain

Craniofacial superimposition is a forensic process where photographs or video shots of a missing person are compared with the skull that is found. By projecting both photographs on top of each other (or, even better, matching a scanned three-dimensional skull model against the face photo/video shot), the forensic anthropologist can try to establish whether that is the same person. The whole process is influenced by inherent uncertainty mainly because two objects of different nature (a skull and a face) are involved. In previous work, we categorized the different sources of uncertainty and introduced the use of imprecise landmarks to tackle most of them. In this paper, we propose a novel approach, a cooperative coevolutionary algorithm, to deal with the use of imprecise cephalometric landmarks in the skull-face overlay process, the main task in craniofacial superimposition. Following this approach we are able to look for both the best projection parameters and the best landmark locations at the same time. coevolutionary skull-face overlay results are compared with our previous fuzzy-evolutionary automatic method. Six skull-face overlay problem instances corresponding to three real-world cases solved by the Physical Anthropology Lab at the University of Granada (Spain) are considered. Promising results have been achieved, dramatically reducing the run time while improving the accuracy and robustness.

关键词： Forensic identification Craniofacial superimposition skull-face overlay Fuzzy landmarks Fuzzy distances Evolutionary algorithms CMA-ES coevolutionary algorithm Genetic fuzzy systems

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coevolutionary makespan optimisation through different ranking methods for the fuzzy flexible job shop

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FUZZY SETS AND SYSTEMS 2015年 278卷 81-97页

作者： Jose Palacios, Juan Gonzalez-Rodriguez, Ines Vela, Camino R. Puente, Jorge Univ Oviedo Dept Comp Oviedo Spain Univ Cantabria Dept Math Stat & Comp Cantabria Spain

In this paper we tackle a variant of the flexible job shop scheduling problem with uncertain task durations modelled as fuzzy numbers, the fuzzy flexible job shop scheduling problem or FfJSP in short. To minimise the schedule's fuzzy makespan, we consider different ranking methods for fuzzy numbers. We then propose a cooperative coevolutionary algorithm with two different populations evolving the two components of a solution: machine assignment and task relative order. Additionally, we incorporate a specific local search method for each population. The resulting hybrid algorithm is then evaluated on existing benchmark instances, comparing favourably with the state-of-the-art methods. The experimental results also serve to analyse the influence in the robustness of the resulting schedules of the chosen ranking method. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Flexible job shop scheduling Robustness Local search coevolutionary algorithm Ranking of fuzzy numbers Fuzzy processing

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coevolutionary Workflow Scheduling in a Dynamic Cloud Environment

Coevolutionary Workflow Scheduling in a Dynamic Cloud Enviro...

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Int Joint Conf on Int Conference on Soft Comp Models in Ind and Environm Applicat / Int Conf on Computat Intelligence in Secur Informat Syst / Int Conf on European Transnat Educ

作者： Nasonov, Denis Melnik, Mikhail Radice, Anton ITMO Univ St Petersburg Russia

ISBN: (纸本)9783319473642;9783319473635

In this paper, we present a new coevolutionary algorithm for workflow scheduling in a dynamically changing environment. Nowadays, there are many efficient algorithms for workflow execution planning, many of which are based on the combination of heuristic and metaheuristic approaches or other forms of hybridization. The coevolutionary genetic algorithm (CGA) offers an extended mechanism for scheduling based on two principal operations: task mapping and resource configuration. While task mapping is a basic function of resource allocation, resource configuration changes the computational environment with the help of the virtualization mechanism. In this paper, we present a strategy for improving the CGA for dynamically changing environments that has a significant impact on the final dynamic CGA execution process.

关键词： Workflow scheduling coevolutionary algorithm Genetic algorithm Virtualization

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Dynamic Resources Configuration for coevolutionary Scheduling of Scientific Workflows in Cloud Environment 12th

Dynamic Resources Configuration for Coevolutionary Schedulin...

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12th Int Conf Soft Computing Models in Industrial and Environmental Applications (SOCO) / 10th Int Conf on Computational Intelligence in Security for Information Systems (CISIS) / 8th Int Conf on European Transnational Education

作者： Visheratin, Alexander A. Melnik, Mikhail Nasonov, Denis ITMO Univ St Petersburg Russia

ISBN: (纸本)9783319671802;9783319671796

Modern composite scientific applications, also called scientific workflows, require large processing capacities. Cloud environments provide high performance and flexible infrastructure, which can be easily employed for workflows execution. Since cloud resources are paid in the most cases, there is a need to utilize these resources with maximal efficiency. In this paper we propose dynamic resources coevolutionary genetic algorithm, which extends previously developed coevolutionary genetic algorithm for dynamic cloud environment by changing computational capacities of execution nodes on runtime. This method along with using two types of chromosomes - mapping of tasks on resources and resources configuration - allows to greatly extend the search space of the algorithm. Experimental results demonstrate that developed algorithm is able to generate solutions better than other scheduling algorithms for a variety of scientific workflows.

关键词： Workflow scheduling Dynamic coevolution coevolutionary algorithm Genetic algorithm Virtualization

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coevolutionary Procedural Generation of Battle Formations in Massively Multiplayer Online Strategy Games 13

Coevolutionary Procedural Generation of Battle Formations in...

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13th Brazilian Symposium on Computer Games and Digital Entertainment (SBGAMES)

作者： Ruela, Andre Siqueira Guimaraes, Frederico Gadelha Univ Fed Minas Gerais Grad Program Elect Engn Av Antonio Carlos 6627 Belo Horizonte MG Brazil Univ Fed Minas Gerais Dept Elect Engn Belo Horizonte MG Brazil

ISBN: (纸本)9781479980659

This paper presents a coevolutionary genetic algorithm for the development of battle formations in Massively Multiplayer Online Real-Time Strategy games. We consider the context of the game Call of Roma, where the battles are turn based and two sides fight each other, each side involving one or more players. The coevolutionary genetic algorithm takes as input a predetermined battle formation, presented by the defense side, and returns a battle formation adapted for the Attack side. The algorithm aims to maximize the battle performance, which is a subjective concept that varies from player to player. The individual encodes the various characteristics of the fighting heroes. We tested the algorithm over a cloud-computing platform, considering test cases modeled by several active players. The results illustrate that the proposed algorithm is able to find a victorious solution for the Attack team, even when it is under unfavorable conditions.

关键词： Massively Multiplayer Online Real-time Strategy coevolutionary algorithm Procedural Content Generation

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