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4th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM)

作者： Notsu, Akira Saito, Koki Nohara, Yuhumi Ubukata, Seiki Honda, Katsuhiro Osaka Prefecture Univ Grad Sch Engn Naka Ku Sakai Osaka 5998531 Japan

ISBN: (纸本)9783319251356;9783319251349

In this paper, a novel method for the discrete optimization problem is proposed based on the UCB algorithm. Definition of the neighborhood in the search space of the problem easily affects the performance of the existing algorithms because they do not well take into account the dilemma of exploitation and exploration. To optimize the balance of exploitation and exploration, we divide the search space into several grids to reconsider the discrete optimization problem as a Multi-Armed Bandit problem, and therefore the UCB algorithm is directly introduced for the balancing. We proposed a UCB-grid area search and conducted numerical experiments on the 0-1 Knapsack problem. Our method showed stable results in different environments.

关键词： discrete optimization problem Multi-armed bandit problem GA UCB

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A hybrid algorithm using particle swarm optimization for solving transportation problem

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NEURAL COMPUTING & APPLICATIONS 2020年第15期32卷 11699-11716页

作者： Singh, Gurwinder Singh, Amarinder IK Gujral Punjab Tech Univ Jalandhar Jalandhar Punjab India BBSBEC Dept Appl Sci Fatehgarh Sahib Punjab India

Particle swarm optimization (PSO) is a well-known population-based stochastic optimization algorithm intended by collective and communicative behavior of bird flocks looking for food. Being a very powerful tool for obtaining the global optimal solution, PSO has experienced a multitude of enhancements during the last three decades. The algorithm has been modified, hybridized and extended by various authors in terms of structural variations, parameters selection and tuning, convergence analysis and meta-heuristics. In this article, hybridized PSO has been proposed to solve balanced transportation problem, a discrete optimization problem, of any number of decision variables converging to the global optima. Two additional modules have been embedded within the PSO, in order to repair the negative and/or fractional values of the decision variables, and tested with variants of parameters present therein. The proposed algorithm generates an optimal solution even without considering the rigid conditions of the traditional techniques. The paper compares the performance of different variants of inertia weight, acceleration coefficients and also the population size with respect to the convergence to the optimal solution. The performance of the proposed algorithm is statistically validated using the pairedttest.

关键词： discrete optimization problem Combinatorial optimization problem Transportation problem Particle swarm optimization Optimal solution

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Restart technology for solving discrete optimization problems

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CYBERNETICS AND SYSTEMS ANALYSIS 2000年第5期36卷 659-666页

作者： Sergienko, IV Shilo, VP Roshchin, VA Natl Acad Sci Ukraine Inst Cybernet Kiev Ukraine

The RESTART technology is proposed, which allows us to speed up the solution of discrete optimization problems.

关键词： RESTART technology discrete optimization problem solution time

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Quasistability of a vector trajectory majority optimization problem

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MATHEMATICAL NOTES 2002年第1-2期72卷 34-42页

作者： Emelichev, VA Stepanishina, YV Belarusian State Univ Minsk 220050 BELARUS

We consider a multicriteria combinatorial problem with majority optimality principle whose particular criteria are of the form MINSUM, MINMAX, and MINMIN. We obtain a lower attainable bound for the radius of quasistability of such a problem in the case of the Chebyshev norm on the space of perturbing parameters of the vector criterion. We give sufficient conditions for the quasistability of the problem;these are also necessary in the case of linear special criteria.

关键词： discrete optimization problem multicriteria optimization lower bound for the radius of quasistability necessary and sufficient conditions for the quasistability of a majority optimization problem

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Polynomial-Time Solvability of One optimization problem Induced by Processing and Analyzing Quasiperiodic ECG and PPG Signals 1

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10th International Conference on optimization and Applications (OPTIMA)

作者： Kel'manov, Alexander Khamidullin, Sergey Mikhailova, Liudmila Ruzankin, Pavel Sobolev Inst Math 4 Koptyug Ave Novosibirsk 630090 Russia Novosibirsk State Univ 2 Pirogova St Novosibirsk 630090 Russia

ISBN: (数字)9783030386030

ISBN: (纸本)9783030386030;9783030386023

This paper is devoted to an unexplored discrete optimization problem, which can be interpreted as a problem of least mean squares approximation of some observed discrete-time signal (a numerical time series) by an unobservable quasiperiodic (almost periodic) pulse signal generated by a pulse with a given pattern (reference) shape. Quasiperiodicity is understood, first, in the sense of admissible fluctuations of the interval between repetitions of the reference pulse, and second, in the sense of admissible nonlinear time expansions of its reference shape. Such problems are common in biomedical applications related to monitoring and analyzing electrocardiogram (ECG), photoplethysmogram (PPG), and several other signals. In the optimization model, the number of generated (admissible or approximating) quasiperiodic pulse sequences grows exponentially with the duration of the discrete-time signal (i.e., with the number of points in the time series). The size of the admissible solutions set also grows exponentially. However, despite that exponential growth, we have constructively proved the optimization problem polynomial-time solvability. Namely, we propose an algorithm that finds an optimal solution to the problem in O(T-max(3) N) time;where N is the duration of the observed signal (the number of points in the time series), T-max <= N is a positive integer number which bounds the fluctuations of the repetition period. If T-max is a part of the input, then the algorithm's running time is O( N-4), i.e., the algorithm is polynomial. If T-max is a fixed parameter (that is typical for applications), then the running-time of the algorithm is O(N), i.e., the algorithm is linear in time. Numerical simulation examples demonstrate the robustness of the algorithm in the presence of additive noise.

关键词： discrete optimization problem Polynomial-time solvability Linear-time algorithm and Pulse train signal processing Quasiperiodic ECG PPG

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Neighborhood Hybrid Structure for discrete optimization problems

Neighborhood Hybrid Structure for Discrete Optimization Prob...

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7th IEEE Electronics, Robotics and Automotive Mechanics Conference (CERMA)

作者： Antonio Cruz-Chavez, Marco Martinez-Oropeza, Alina Serna Barquera, Sergio A. Univ Autonoma Estado Morelos CIICAp Av Univ 1001 Cuernavaca 62209 Morelos Mexico

ISBN: (纸本)9780769542041

In this paper a comparative analysis of a neighborhood structures group are presented, including a hybrid structure, which arises of a combination of this set of structures. The efficiency and effectiveness of each structure was tested using the Classical Symmetric Travelling Salesman problem. This study identifies the neighborhood structure that allows performing a better exploration and exploitation of the space solutions to discrete optimization problems. A neighborhood hybrid structure proposed has better performance comparing with other techniques, this is experimentally proved;in addition a competitive efficiency is shown.

关键词： classical symmetric travelling salesman problem discrete optimization problem neighborhood hybrid structure travelling salesman problems

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An application of fruit fly optimization algorithm for traveling salesman problem 8th

An application of fruit fly optimization algorithm for trave...

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8th International Conference on Advances in Information Technology (IAIT)

作者： Iscan, Hazim Gunduz, Mesut Selcuk Univ Comp Engn Dept Konya Turkey

In this study, an application of fruit fly optimization algorithm (FOA) is presented. FOA is one of the recently proposed swarm intelligence optimization algorithms used to solve continuous complex optimization problems. FOA has been invented by Pan in 2011 and it is based on the food search behavior of fruit flies. The FOA has a simple framework and it is easy to implement for solving optimization problem with different characteristics. The FOA is also a robust and fast algorithm and some researchers used FOA to solve discrete optimization problems. In this study, a new modified FOA is proposed for solving the well-known traveling salesman problem (TSP) which is one of the most studied discrete optimization problems. In basic FOA, there are two basic phases, one of them is osphresis phase and the other is vision phase. In the modified version of FOA the ospherisis phases kept as it is and for vision phase two different methods developed. In vision phase, the first half of the city arrangement matrix is updated according to first %30 part of best solutions of the ospheresis phase. The other half of the city arrangement matrix is randomly reproduced because of the possibility that initial solutions are far from the optimum. According to the results, travelling salesman problem can be solved with FOA as an alternative method. For big scale problems, it needs some improvements. (c) 2017 The Authors. Published by Elsevier B.V.

关键词： Fruit fly optimization algorithm metaheuristic FOA FFOA traveling salesman problem discrete optimization problem

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Some special heuristics for discrete optimization problems

Some special heuristics for discrete optimization problems

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8th International Conference on Enterprise Information Systems (ICEIS 2006)

作者： Melnikov, Boris Radionov, Alexey Gumayunov, Viktor Togliatti State Univ Dept Math & Informat Sci Tolyatti 445667 Russia

ISBN: (纸本)9728865422

In previous paper we considered some heuristic methods of decision-making for various discrete optimization problems;all these heuristics should be considered as the combination of them and form a common multi-heuristic approach to the various problems. And in this paper, we begin to consider local heuristics, which are different for different problems. At first, we consider two problems of minimization: for nondeterministic finite automata and for disjunctive normal forms. Our approach can be considered as an alternative to the methods of linear programming, multi-agent optimization, and neuronets.

关键词： anytime algorithm discrete optimization problem local heuristics minimization nondeterministic finite automata disjunctive normal forms

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An application of fruit fly optimization algorithm for traveling salesman problem

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Procedia Computer Science 2017年 111卷 58-63页

作者： Hazim Iscan Mesut Gunduz Computer Engineering Department Selcuk University Konya Turkey

关键词： Fruit fly optimization algorithm metaheuristic FOA FFOA traveling salesman problem discrete optimization problem

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Drilling Path optimization by Optimal Foraging Algorithm

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2018年第7期14卷 2847-2856页

作者： Zhang, Wei-Bo Zhu, Guang-Yu Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350002 Fujian Peoples R China

Drilling path optimization (DPO) is a crucial issue in current manufacturing systems. In this paper, an optimization algorithm named the optimal foraging algorithm (OFA) is presented based on optimal foraging theory to solve the sequencing problem when many holes must be drilled. The objective of this study is to identify the optimal sequence of drilling operations using OFA to minimize the total processing cost. Since the drilling sequence optimization problem is considered a discrete optimization problem, five operators that can address the integer-encoding vector are built for OFA;thus, a discrete version of OFA is presented. The performance of OFA is evaluated against four other baseline algorithms in solving five real-world problems. The results, including the optimal solutions, the Kruskal-Wallis test, CPU time and the evolution curves, demonstrate that OFA improves the solution by minimizing nonproductive time. OFA is verified as a feasible method for solving DPO problems.

关键词： discrete optimization problem drilling path optimization (DPO) hole-making problem optimal foraging algorithm (OFA) optimal foraging theory.

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