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18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Perera, Kokila Kasuni Neumann, Frank Neumann, Aneta Univ Adelaide Sch Comp & Math Sci Optimisat & Logist Adelaide SA Australia

ISBN: (纸本)9783031700545;9783031700552

Uncertainties in real-world problems impose a challenge in finding reliable solutions. If mishandled, they can lead to suboptimal or infeasible solutions. Chance constraints are a natural way to capture uncertain problem parameters. They model probabilistic constraints involving the stochastic parameters and an upper bound of probability that mimics the confidence level of the solution. We focus on the knapsack problem with stochastic profits to guarantee a certain level of confidence in the profit of the solutions. We present a bi-objective fitness formulation that uses expected profit and standard deviation to capture the chance constraints. This formulation enables optimising the problem independent of a specific confidence level. We evaluate the proposed fitness formulation using well-known evolutionary algorithms GSEMO, NSGA-II and MOEA/D. Moreover, we introduce a filtering method that refines the interim populations based on the confidence levels of its solutions. We evaluate this method by applying it along with GSEMO to improve the quality of its population during optimisation. We conduct extensive experiments to show the effectiveness of these approaches using several benchmarks and present a detailed analysis of the results.

关键词： evolutionary algorithms Multi-objective optimisation Chance constraints

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Exploring a Promising Region and Enhancing Decision Space Diversity for Multimodal Multi-Objective Optimization

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Tsinghua Science and Technology 2024年第2期29卷 325-342页

作者： Fei Ming Wenyin Gong School of Computer Science China University of GeosciencesWuhan 430074China

During the past decade,research efforts have been gradually directed to the widely existing yet less noticed multimodal multi-objective optimization problems(MMOPs)in the multi-objective optimization ***,researchers have begun to investigate enhancing the decision space diversity and preserving valuable dominated solutions to overcome the shortage caused by a preference for objective space ***,many existing methods still have limitations,such as giving unduly high priorities to convergence and insufficient ability to enhance decision space *** overcome these shortcomings,this article aims to explore a promising region(PR)and enhance the decision space diversity for handling *** traditional methods,we propose the use of non-dominated solutions to determine a limited region in the PR in the decision space,where the Pareto sets(PSs)are included,and explore this region to assist in solving ***,we develop a novel neighbor distance measure that is more suitable for the complex geometry of PSs in the decision space than the crowding *** on the above methods,we propose a novel dual-population-based coevolutionary *** studies on three benchmark test suites demonstrates that our proposed methods can achieve promising performance and versatility on different *** effectiveness of the proposed neighbor distance has also been justified through comparisons with crowding distance methods.

关键词： multimodal multi-objective optimization evolutionary algorithms promising region neighbor distance decision space coevolution

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Embedding a Repair Operator in evolutionary Single and Multi-objective algorithms - An Exploitation-Exploration Perspective 11th

Embedding a Repair Operator in Evolutionary Single and Multi...

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11th International Conference on evolutionary Multi-Criterion Optimization, EMO 2021

作者： Deb, Kalyanmoy Mittal, Sukrit Saxena, Dhish Kumar Goodman, Erik D. BEACON NSF Center and Michigan State University East Lansing United States Indian Institute of Technology Roorkee Roorkee India

ISBN: (纸本)9783030720612

evolutionary algorithms (EAs) are population-based search and optimization methods whose efficacy strongly depends on a fine balance between exploitation caused mainly by its selection operators and exploration introduced mainly by its variation (crossover and mutation) operators. An attempt to improve an EA’s performance by simply adding a new and apparently promising operator may turn counter-productive, as it may trigger an imbalance between the exploitation-exploration trade-off. This necessitates a proper understanding of mechanisms to restore the balance while accommodating a new operator. In this paper, we introduce a new repair operator based on an AI-based mapping between past and current good population members to improve an EA’s convergence properties. This operator is applied to problems with different characteristics, including single-objective (with single and multiple global optima) and multi-objective problems. The focus in this paper is to highlight the importance of restoring the exploitation-exploration balance, when a new operator is introduced. We show how different combinations of problems and EA characteristics pose different opportunities for restoration of this balance, enabling the repair-based EAs/EMOs to outperform the original EAs/EMOs in most cases. © 2021, Springer Nature Switzerland AG.

关键词： evolutionary algorithms

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A Sequence Fine-Tuning Strategy Based evolutionary Algorithm for Solving Project Scheduling Problems 6

A Sequence Fine-Tuning Strategy Based Evolutionary Algorithm...

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6th International Conference on Data-Driven Optimization of Complex Systems (DOCS)

作者： Dong, Zhengxuan Shao, Shuai Tian, Ye Anhui Univ Inst Phys Sci Hefei Peoples R China Anhui Univ Inst Informat Technol Hefei Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China

ISBN: (纸本)9798350377859;9798350377842

As artificial intelligence technology advances, the industrial landscape has been gradually transitioning from manual labor to automated manufacturing processes. This shift has highlighted the growing tension between production scheduling efficiency and cost considerations, culminating in the emergence of Project Scheduling Problems (PSP). Since the emergence of this problem, there have been many ways to deal with this kind of problems, but most of the existing algorithms cannot be used in production problems, and thus cannot provide enterprises with more considerable benefits and costs. Therefore, this paper takes datasets from an actual production enterprise and solves the corresponding PSP problems, by means of designing a multi-objective optimization algorithm based on the characteristics of the PSP problems. Based on a sequence encoding, the proposed algorithm designs a repair strategy for decision variables according to various constraints of the PSP problems, and adds a sequence fine-tuning strategy based on sequence constraints, ensuring the diversity of solutions and improving the feasibility and stability of understanding. We perform experiments and analysis on several datasets involving 138 to 1 380 decision variables, and the results show that the proposed algorithm is more effective than several existing multi-objective evolutionary algorithms.

关键词： evolutionary algorithms sequence optimization multi-objective optimization

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A Two-Stage Repair Strategy Based evolutionary Algorithm for Solving Resource-Constrained Project Scheduling Problems 6

A Two-Stage Repair Strategy Based Evolutionary Algorithm for...

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6th International Conference on Data-Driven Optimization of Complex Systems (DOCS)

作者： Xie, Guohui Shao, Shuai Tian, Ye Anhui Univ Inst Phys Sci Hefei Peoples R China Anhui Univ Inst Informat Hefei Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China

ISBN: (纸本)9798350377859;9798350377842

The Resource-Constrained Project Scheduling Problems (RCPSPs) are considered highly challenging NP-hard problems in the field of project management. The complexity of this type of problems lies in the limited availability of resources and the interdependence of tasks, making it very difficult to find a solution that satisfies all constraints while optimizing objectives using random search paradigms. To address this issue, this paper proposes a novel evolutionary algorithm that employs a two-stage repair strategy to enhance its constraint satisfaction ability in solving large-scale RCPSPs. The first stage of the heuristic strategy aims to quickly identify and repair solutions that violate time constraints, thereby rapidly reducing the number of infeasible solutions in the search space. The second stage applies more refined heuristic rules to further optimize solutions that violate resource constraints, in hopes of finding solutions closer to the global optimum. The design of this two-stage repair strategy increases the likelihood of finding high-quality solutions in complex search spaces. To validate the effectiveness of the proposed algorithm, this paper conducted extensive experiments on nine real-world datasets. Compared with existing advanced evolutionary algorithms, the experimental results demonstrate that the proposed algorithm has excellent performance.

关键词： evolutionary algorithms single-objective optimization constrained optimization

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Applying an evolutionary Algorithm to Minimize Teleportation Costs in Distributed Quantum Computing 5

Applying an Evolutionary Algorithm to Minimize Teleportation...

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2024 International Conference on Quantum Computing and Engineering

作者： Suenkel, Leo Dawar, Manik Gabor, Thomas Ludwig Maximilians Univ Munchen Munich Germany

ISBN: (纸本)9798331541378

By connecting multiple quantum computers (QCs) through classical and quantum channels, a quantum communication network can be formed. This gives rise to new applications such as blind quantum computing, distributed quantum computing, and quantum key distribution. In distributed quantum computing, QCs collectively perform a quantum computation. As each device only executes a sub -circuit with fewer qubits than required by the complete circuit, a number of small QCs can be used in combination to execute a large quantum circuit that a single QC could not solve on its own. However, communication between QCs may still occur. Depending on the connectivity of the circuit, qubits must be teleported to different QCs in the network, adding overhead to the actual computation;thus, it is crucial to minimize the number of teleportations. In this paper, we propose an evolutionary algorithm for this problem. More specifically, the algorithm assigns qubits to QCs in the network for each time step of the circuit such that the overall teleportation cost is minimized Moreover, network -specific constraints such as the capacity of each QC in the network can be taken into account. We run experiments on random as well as benchmarking circuits and give an outline on how this method can be adjusted to be incorporated into more realistic network settings as well as in compilers for distributed quantum computing. Our results show that an evolutionary algorithm is well suited for this problem when compared to the graph partitioning approach as it delivers better results while simultaneously allows the easy integration and consideration of various problem -specific constraints.

关键词： Distributed Quantum Computing evolutionary algorithms Quantum Networks Quantum Teleportation Cost

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On Convergence of evolutionary algorithms Powered by Non-random Generators 1

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13th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Zelinka, Ivan Davendra, Donald Senkerik, Roman Pluhacek, Michal Oplatkova, Zuzana Kominkova Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava 70833 Czech Republic Tomas Bata Univ Zlin Fac Appl Informat Zlin 76001 Czech Republic

ISBN: (数字)9783319071732

ISBN: (纸本)9783319071725;9783319071732

Inherent part of evolutionary algorithms that are based on Darwin theory of evolution and Mendel theory of genetic heritage, are random processes that are used in every evolutionary algorithm like genetic algorithms etc. In this paper we present experiments (based on our previous) of selected evolutionary algorithms and test functions demonstrating impact of non-random generators on performance of the evolutionary algorithms. In our experiments we used differential evolution and SOMA algorithms with functions Griewangk and Rastrigin. We use n periodical deterministic processes (based on deterministic chaos principles) instead of pseudorandom number generators and compare performance of evolutionary algorithms powered by those processes and by pseudorandom number generators. Results presented here has to be understand like numerical demonstration rather than mathematical proofs. Our results (reported sooner and here) suggest hypothesis that certain class of deterministic processes can be used instead of random number generators without lowering the performance of evolutionary algorithms.

关键词： evolutionary algorithms non-random generators pseudorandom generators deterministic chaos

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A Relation-and-Regression-assisted evolutionary Algorithm for Expensive Constrained Multi-objective Optimization 6

A Relation-and-Regression-assisted Evolutionary Algorithm fo...

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6th International Conference on Data-Driven Optimization of Complex Systems (DOCS)

作者： Chen, Siyu Zhang, Jinyuan Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Expensive constrained multi-objective optimization problems involve computationally expensive objectives and constraints, which impose stiff challenges on traditional evolutionary algorithms to optimize within limited function evaluations. To address this, some surrogate-assisted constrained multi-objective evolutionary algorithms have been proposed, where surrogate models are constructed to replace expensive function evaluations. However, most existing surrogate models are either regression or classification models, which are liable to poor reliability in approximating complicated constraints. In this paper, a relation-and-regression-assisted constrained multi-objective evolutionary algorithm, named RCMOEA, is proposed. In RCMOEA, each regression model is constructed to approximate each objective function, and each relation model is built to learn the relation of constraint values between any two solutions. Based on the constructed surrogate models, a relation-and-regression-based constrained Pareto dominance, denoted as RCPD, is proposed to compare solution pairs. By adopting RCPD as the dominance criterion, the RCPD-based selection strategy is proposed for selecting offspring solutions. Also, the distance-based infill sampling strategy is proposed to preserve the diversity of solutions. Experimental results demonstrate the superiority of RCMOEA over the compared algorithms.

关键词： Expensive constrained multi-objective optimization evolutionary algorithms surrogate models

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Two novel stable phases of polonium oxides from first-principles evolutionary algorithm

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COMPUTATIONAL CONDENSED MATTER 2024年 40卷

作者： Menad, A. Zaoui, A. Ferhat, M. LPMF Univ Sci & Technol Oran Dept Genie Phys Oran Algeria Ecole Normale Super Mostaganem Dept Phys Mostaganem Algeria Univ Artois Univ Lille IMT Lille Douai JUNIAULR 4515 LGCgELab Genie Civil & Geoenvironm F-59000 Lille France

Oxygen prominently displays a strong propensity to efficiently form stable molecules by establishing ionic/covalent bonds with a broad array of elements within the Mendeleev Table. That gives rise to a diverse spectrum of oxides that hold pivotal significance in contemporary optoelectronics, mineralogy, biological entities, and atmospheric constitution. We explore here the feasibility of polonium-oxygen compound formation, employing a first-principle evolutionary algorithm. The obtained structural predictions yield two distinct phases of PoO2, exhibiting respectively thermodynamic stability and metastability, specifically the cubic (Fm 3 m) and orthorhombic (Pmn21) crystal structures. Phonon calculations have unequivocally substantiated the dynamical stability inherent in these PoO2 structures. The Po-O system, characterized by its unusual physical-chemical attributes, presents noteworthy features in electronic behavior, bonding interactions, and dynamical properties. Both the Fm 3 m and the Pmn21 phases exhibit semiconductor behavior, each displaying a relatively substantial indirect bandgap of 2.11 eV and 2.50 eV, respectively, within the cubic and orthorhombic crystals. To unravel the bonding nature of PoO2, we employ a suite of analytical tools, including electronic density of states, Bader charge analysis, and electron localization function. These analyses collectively unveil a transfer of electrons from polonium to oxygen, thereby elucidating the coexistence of significant ionic and partial covalent Po-O bonds within the Po-O system.

关键词： Bulk crystal phases of polonium oxide (Po-O) system DFT evolutionary algorithms

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Hybrid evolutionary Approach to Team Building using PROMETHEE II 24th

Hybrid Evolutionary Approach to Team Building using PROMETHE...

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24th International Conference on Group Decision and Negotiation (GDN)

作者： Stavrou, Georgios Adamidis, Panagiotis Papathanasiou, Jason Tarabanis, Konstantinos Univ Macedonia Dept Informat & Elect Engn Thessaloniki 57400 Greece Int Hellen Univ Egnatias 156 Thessaloniki 54636 Greece Univ Macedonia Egnatias 156 Thessaloniki 54636 Greece

ISBN: (纸本)9783031593727;9783031593734

Multi-Criteria Decision Making (MCDM) methods, such as PROMETHEE II, play a crucial role in complex decision-making scenarios, including team formation. However, they face scalability challenges as the number of criteria and options increases. This paper introduces a novel Hybrid evolutionary Algorithm integrated with PROMETHEE II, specifically designed for team formation. This hybrid approach combines the exploration power of evolutionary algorithms and the decision-making capabilities of PROMETHEE II, aiming to improve both performance and scalability in decision-making processes. Initial experiments demonstrate significant improvements in both solution quality and scalability compared to existing methods facing similar challenges. This research enables the creation of more efficient and effective team formation in complex decision-making scenarios.

关键词： Multi-Criteria Decision Making Team Formation evolutionary algorithms PROMETHEE II Algorithmic Efficiency Computational Optimization Team Composition Analysis Decision Support Systems

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