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9th International Workshop on Structured Object-Oriented Formal Language (SOFL)+The Modeling, Simulation and Verification Language (MSVL) for Reliability and Security

作者： Pan, Guoteng Li, Mengjun Ou, Guodong Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9783030414184;9783030414177

Solving constraint optimization problems is widely applicable in computer science. The computer algebra system Mathematica provides MaxValue, MinValue and other functions to solve constraint optimization problems. However many cases of constraint optimization problems cannot be solved by these functions directly. In this paper, based on these Mathematica functions and abstraction, a practical approach is presented for computing the upper bounds and the lower bounds of the optimization values of constraint optimization problems. The optimization values of many constraint optimization problems, which cannot be solved by Mathematica functions directly, can be computed automatically by using the approach presented in this paper. The experimental results demonstrate the practicality of this approach.

关键词： constraint optimization problems Upper and lower bounds Mathematica Abstraction

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Pareto-Optimal Trace Generation from Declarative Process Models

Pareto-Optimal Trace Generation from Declarative Process Mod...

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21st International Conference on Business Process Management (BPM)

作者： Diaz, Juan F. Lopez, Hugo A. Quesada, Luis Rosero, Juan C. Univ Valle Cali Colombia Tech Univ Denmark Lyngby Denmark Insight Ctr Data Analyt Cork Ireland

ISBN: (纸本)9783031509735;9783031509742

Declarative process models (DPMs) enable the description of business process models with a high level of flexibility by being able to describe the constraints that compliant traces must abide by. In this way, a well-formed declarative specification generates a family of compliant traces. However, little is known about the difference between different compliant traces, as the only criterion used for comparison is satisfiability. In particular, we believe that not all compliant traces are alike: some might be sub-optimal in their resource usage. In this work, we would like to support users of DPMs in the selection of compliant and optimal traces. In particular, we use Dynamic Condition Response (DCR) graphs as our language to represent DPMs, extending it with a parametric definition of costs linked to events. Multiple types of cost imply that different traces might be optimal, each according to a different cost dimension. We encode cost-effective finite trace generation as a constraint Optimisation Problem (COP) and showcase the feasibility of the implementation via an implementation in MiniZinc. Our initial benchmarks suggest that the implementation is capable of providing answers efficiently for processes of varying size, number of constraints, and trace length.

关键词： Declarative Process Models DCR graphs constraint optimization problems Multi-Objective optimization

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Minimization of fuel cost for generator systems with valve point effect using modified PSO

Franklin Open

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Franklin Open 2024年 7卷

作者： Prakash, Om Rai, Amrita Das, Saumya Gopal, Yatindra Singh, Hare Ram Somassoundaram, T. Department of Electronics and Communication Engineering Sri Venkateswara College of Engineering & Technology (Autonomous) Andhra Pradesh Chittoor India Department of Electronics and Communication Engineering LLOYD Institute of Engineering and Technology UP Greater Noida India Department of Information Technology Sikkim Manipal Institute of Technology Sikkim Manipal University India Department of Electrical and Electronics Engineering Sri Venkateswara College of Engineering & Technology (Autonomous) Andhra Pradesh Chittoor India Department of Computer Science and Engineering Sri Venkateswara College of Engineering & Technology (Autonomous) Andhra Pradesh Chittoor India

The goal of this research is to lay the groundwork for understanding the particle swarm optimization technique. The applicability of this technique is proven by solving a variety of constraint and unconstraint optimization problems. During the operation of the power system, economic load dispatch is a vital optimization activity for allocating generation between the committed units to divide the power while meeting various system constraints economically. In this work, the modified PSO method's application to the electrical power system's economic load dispatch problem on 3-generator, 5-generator and 13-generator systems are also investigated. In this research work Exterior penalty method is applied to convert constraint optimization problem into unconstraint optimization problem © 2024 The Author(s)

关键词： constraint optimization problems Exterior penalty method PSO and ELD Unconstraint optimization

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Inverse problem for a class of nonlinear elliptic equations with entropy solution

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NONLINEAR ANALYSIS-REAL WORLD APPLICATIONS 2019年 46卷 137-156页

作者： Ellabib, Abdellatif Ouakrim, Youssef Univ Cadi Ayyad Fac Sci & Tech Lab Math Appl & Informat Ave Abdelkarim Elkhttabi BP 549 Marrakech Morocco

We study a nonlinear ill posed inverse problem arising from the identification of nonlinearities in elliptic partial differential equations with Ll data. Using duality regularization techniques and optimal control principle, we approach the ill posed inverse problem by a constraint optimization problem. Then, we establish the well posedness and sensibility of the regularized solutions with respect to the given data. We finish by an application in image restoration, precisely, we use our approach to find an optimal choice of the regularization function in a weighted total variation model. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Identification Nonlinear inverse problems Nonlinear partial differential equations Entropy solutions constraint optimization problems

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AlleAlle: Bounded Relational Model Finding with Unbounded Data

AlleAlle: Bounded Relational Model Finding with Unbounded Da...

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ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward) part of SPLASH Conference

作者： Stoel, Jouke van der Storm, Tijs Vinju, Jurgen J. CWI Amsterdam Netherlands Univ Groningen Groningen Netherlands TUe Eindhoven Netherlands

ISBN: (纸本)9781450369954

Relational model finding is a successful technique which has been used in a wide range of problems during the last decade. This success is partly due to the fact that many problems contain relational structures which can be explored using relational model finders. Although these model finders allow for the exploration of such structures they often struggle with incorporating the non-relational elements. In this paper we introduce ALLEALLE, a method and language that integrates reasoning on both relational structure and non-relational elements -the data- of a problem. By combining first order logic with Codd's relational algebra, transitive closure, and optimization criteria, we obtain a rich input language for expressing constraints on both relational and scalar values. We present the semantics of ALLEALLE and the translation of ALLEALLE specifications to SMT constraints, and use the off-the-shelf SMT solver Z3 to find solutions. We evaluate ALLEALLE by comparing its performance with Kodkod, a state-of-the-art relational model finder, and by encoding a solution to the optimal package resolution problem. Initial benchmarking show that although the translation times of ALLEALLE can be improved, the resulting SMT constraints can efficiently be solved by the underlying solver.

关键词： First order logic relational algebra SMT solvers model finding constraint problems constraint optimization problems

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Triangle-based consistencies for cost function networks

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constraintS 2017年第2期22卷 230-264页

作者： Nguyen, Hiep Bessiere, Christian de Givry, Simon Schiex, Thomas Univ Toulouse MIAT UR 875 INRA Castanet Tolosan France Univ Montpellier Montpellier France

Cost Function Networks (aka Weighted CSP) allow to model a variety of problems, such as optimization of deterministic and stochastic graphical models including Markov random Fields and Bayesian Networks. Solving cost function networks is thus an important problem for deterministic and probabilistic reasoning. This paper focuses on local consistencies which define essential tools to simplify Cost Function Networks, and provide lower bounds on their optimal solution cost. To strengthen arc consistency bounds, we follow the idea of triangle-based domain consistencies for hard constraint networks (path inverse consistency, restricted or max-restricted path consistencies), describe their systematic extension to cost function networks, study their relative strengths, define enforcing algorithms, and experiment with them on a large set of benchmark problems. On some of these problems, our improved lower bounds seem necessary to solve them.

关键词： Cost function networks Weighted CSP constraint optimization problems High order consistencies Restricted path consistency Path inverse consistency Max-restricted path consistency

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ε constrained differential evolution with pre-estimated comparison using gradient-based approximation for constrained optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 44卷 37-49页

作者： Yi, Wenchao Li, Xinyu Gao, Liang Zhou, Yinzhi Huang, Jida Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China SUNY Buffalo Ind & Syst Engn Dept Buffalo NY 14260 USA

Many real-world problems can be categorized as constrained optimization problems. So, designing effective algorithms for constrained optimization problems become more and more important. In designing algorithms, how to guide the individuals moving more efficiently towards the feasible region is one of the most important aspects on finding the optimum of constrained optimization problems. In this paper, we propose an improved constrained differential evolution, which combines with pre-estimated comparison gradient based approximation. The proposed algorithm uses gradient matrix to determine whether the trail vector generated by differential evolution algorithm is worth using the fitness function to evaluate it or not. Pre-estimated comparison gradient based approximation is used as a detector to find the promising offspring and in this way can we guide the individuals moving towards the feasible region. The proposed method is tested both on twenty-four benchmark functions and four well-known engineering optimization problems. Experimental results show that the proposed algorithm is highly competitive in comparing with other state-of-the-art algorithms. The proposed algorithm offers higher accuracy in engineering optimization problems for constrained optimization problems. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Differential evolution algorithm constraint optimization problems epsilon level comparison Pre-estimated comparison gradient based approximation

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Theoretical insights and algorithmic tools for decision diagram-based optimization

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constraintS 2016年第4期21卷 533-556页

作者： Bergman, David Cire, Andre A. Univ Connecticut Sch Business One Univ Pl Stamford CT 06901 USA Univ Toronto Scarborough Dept Management 1265 Mil Trail Toronto ON M1C 1A4 Canada

The use of decision diagrams has recently emerged as a viable general solution approach for solving discrete optimization problems. The decision diagram data structure is used to explicitly represent, either exactly or approximately, the set of feasible solutions to a given problem. Techniques based on decision diagrams have been successfully used on a diverse set of applications, ranging from scheduling to combinatorial optimization, and have often outperformed commercial state-of-the-art constraint programming and integer programming technology. Lacking, however, is a thorough theoretical investigation into the quality of approximate decision diagrams, as well as the development of structured techniques for tightening relaxation bounds provided by approximate decision diagrams, analogously to how cutting-planes are used in integer programming. This paper provides an analysis of the strength of approximate decision diagrams, as well as the description of several bound-tightening procedures for problems with linear objective functions.

关键词： Decision diagrams Discrete optimization constraint satisfaction problems constraint optimization problems

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An Evolved S-band Helical Antenna with 1.8G Bandwidth 11

An Evolved S-band Helical Antenna with 1.8G Bandwidth

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11th International Conference on Natural Computation (ICNC) / 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Zhang, Qing Zhang, Hong Zeng, Sanyou Huanggang Normal Univ Sch Comp Sci Huanggang 438000 Hubei Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781467376792

Differential evolution (DE) is applied to optimize S-band helical antenna in this paper. The helical antenna spires on a paraboloid which is formed by revolving a parabola curve, not as usual as on the surface of a cylinder. And the spaces between two turns are changeable, not usually constant. In this way, the search space for the helical antenna is increased. This helical antenna design is then formulated into a constrained optimization problem (COP). The coefficients of the parabola curve and the spaces between turns are search variables. The design requirements are modeled into objective and constraints. An evolved antenna meets the requirements. It has a wider frequency band and higher gains than the referred one in this paper.

关键词： evolutionary algorithm differential evolution helical antenna constraint optimization problems

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Constrained optimization by the Evolutionary Algorithm with Lower Dimensional Crossover and Gradient-Based Mutation

Constrained Optimization by the Evolutionary Algorithm with ...

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IEEE Congress on Evolutionary Computation

作者： Zhang, Qing Zeng, Sanyou Wang, Rui Shi, Hui Chen, Guang Ding, Lixin Kang, Lishan China Univ Geosci Technol Res Ctr Space Sci & Technol Sch Comp Sci Wuhan 430074 Peoples R China

ISBN: (纸本)9781424418220

This paper proposes a new evolutionary algorithm with lower dimensional crossover and gradient-based mutation for real-valued optimization problems with constraints. The crossover operator of the new algorithm searches a lower dimensional neighbor of the parent points where the neighbor center is the barycenter of the parents, and therefore the new algorithm converges fast. The gradient-based mutation is used to converge fast for the problems with equality constraints and active inequality constraints. And the new algorithm is simple and easy to be implemented. We have used 24 constrained benchmark problems to test the new algorithm. The experimental results show it works better than or competitive to a known effective algorithm.

关键词： Evolutionary algorithm constraint optimization problems Solution dominance

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