Showing 1-25 of 28,206 resultsfor "Index Terms":Dynamic Optimization Problems
"Index Terms":Dynamic Optimization Problems
- Sort
Get notified when new research is published matching your search criteria.
Sort By
Results
Metaheuristics for dynamic combinatorial optimization problems
Many real-world optimization problems are combinatorial optimization problems subject to dynamic environments. In such dynamic combinatorial optimization problems (DCOPs), the objective, decision variables and/or constraints may change over time, and so solving DCOPs is a challenging task. Metaheuristics are a good choice of tools to tackle DCOPs because many metaheuristics are inspired by natural...Show More
Dynamic Multimodal Optimization: A Preliminary Study
Shi Cheng;Hui Lu;Yi-nan Guo;Xiujuan Lei;Jing Liang;Junfeng Chen;Yuhui Shi
2019 IEEE Congress on Evolutionary Computation (CEC)
Year: 2019 | Conference Paper |
Cited by: Papers (5)
The benchmark problems have played a fundamental role in verifying the algorithm's search ability. A dynamic multimodal optimization (DMO) problem is defined as an optimization problem with multiple global optima and characteristics of global optima which are changed during the search process. Two cases are used to illustrate the application scenario of DMO. A set of benchmark functions on DMO, wh...Show More
MOPSO for dynamic feature selection problem based big data fusion
2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Year: 2016 | Conference Paper |
Cited by: Papers (7)
Optimization process occurs in many aspects and areas of everyday life. However, the big use of the internet in recent years caused a complex management of large quantities of data that are stored in many different data sources and optimization attend the domain of big data to optimize multi and dynamic data that stored in a complex dataset including all types of transactions in the data sources. ...Show More
Iterative Discrete Particle Swarm Optimization Algorithm and Its Application to Batch Process Optimization
2009 International Conference on Computational Intelligence and Natural Computing
Year: 2009 | Conference Paper |
Cited by: Papers (1)
To solve dynamic optimization problems of batch processes without state independent and end-point constraints, an iterative discrete particle swarm optimization (IDPSO) algorithm was developed. The main idea of the algorithm was to execute the discrete particle swarm optimization (DPSO) iteratively then the control profile would converge to an optimal one. For the method, the control region and ti...Show More
Exchange Strategies for Multi-Colony Ant Algorithms in Dynamic Environments
Michalis Mavrovouniotis;Changhe Li;Danial Yazdani;Diofantos Handjimitsis
2024 IEEE Congress on Evolutionary Computation (CEC)
Year: 2024 | Conference Paper |
In dynamic optimization problems where optimal solutions change over time, traditional ant colony optimization (ACO) algorithms face limitations. This study explores the adaptation of multi-colony ACO algorithms, known for their enhanced search capabilities in stationary problems, to tackle optimization problems in dynamic environments. Various strategies for exchanging information between colonie...Show More
Travelling Sales Person Problem Using Soft Computing – Genetic Algorithm Techniques
Abhinav Borad;Karnam Akhil;A Madhavi;Sampath Alankritha;Mandalapu Akhil
2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Genetic Algorithms (GAs) is a predominant heuristic technique used to improve the solution space for Travelling Salesman Problem (TSP) and real-time problems in dynamic environments. In this paper various genetic algorithms for TSP are reviewed and deliberated about the statement of a salesman who tries to find out a shortest path from his initial location. The Salesman uses a concept of Hamiltoni...Show More
Dynamic constrained multi-objective model for solving constrained optimization problem
Sanyou Zeng;Shizhong Chen;Jiang Zhao;Aimin Zhou;Zhengjun Li;Hongyong Jing
2011 IEEE Congress of Evolutionary Computation (CEC)
Year: 2011 | Conference Paper |
Cited by: Papers (11)
Constrained optimization problem (COP) is skillfully converted into dynamic constrained multi-objective optimization problem (DCMOP) in this paper. Then dynamic constrained multi-objective evolutionary algorithms (DCMOEAs) can be used to solve the COP problem by solving the DCMOP problem. Seemingly, a complex DCMOEA algorithm is used to solve a relatively simple COP problem. However, the DCMOEA al...Show More
New Dynamic Constrained Optimization PSO Algorithm
2008 Fourth International Conference on Natural Computation
Year: 2008 | Conference Paper |
Cited by: Papers (10)
A new particle swarm optimization (PSO) algorithm solving dynamic constrained optimization problem (DCOP) is proposed in this paper. First, the time period of DCOP was divided into several small estequal subperiods. In each subperiod, the DCOP is approximated by a static constrained optimization problem, Thus, the original DCOP is approximately transformed into several static constrained optimizat...Show More
Improved antlion Optimizer algorithm base on Dynamic random hill-climbing mechanism
2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)
Year: 2020 | Conference Paper |
In this paper, a dynamic random hill-climbing mechanism is proposed to solve the problems that the ant lion optimization algorithm is easy to fall into the local optimal. The algorithm adjusts the position of ant lion by hill-climbing mechanism to enhance the ability to jump out of local optimal. In addition, the global search ability of the algorithm is improved by dynamically hill-climbing mecha...Show More
An improved multi-objective grey wolf optimization algorithm with dynamic chaos local search mechanism
2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC)
Year: 2020 | Conference Paper |
In recently years, the multi-objective grey wolf optimization (MOGWO) has been widely accepted to solve problems in various engineering fields because its less required parameters and excellent optimal performance. However, similar to many intelligent optimization algorithms, the grey wolf optimization algorithm is easy to fall into local optimum. Due to the grey wolf algorithm is shortcomings, an...Show More
Dynamic Multi-Swarm Fractional-best Particle Swarm Optimization for Dynamic Multi-modal Optimization
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2020 | Conference Paper |
Cited by: Papers (1)
While many particle swarm optimization (PSO) algorithms have been developed to find multiple optima to multimodal optimization problems, very few PSO algorithms exist to both find and track multiple optima in dynamically changing search landscapes. This paper presents a novel multi-swarm PSO algorithm, where the number of sub-swarms change dynamically over time to more efficiently adapt to problem...Show More
A Multiple Ant Colony System for Dynamic Vehicle Routing Problem with Time Window
Ashek Ahmmed;Md. Ali Ahsan Rana;Abul Ahsan Md. Mahmudul Haque;Md. Al Mamun
2008 Third International Conference on Convergence and Hybrid Information Technology
Year: 2008 | Conference Paper |
Cited by: Papers (4)
MACS-DVRPTW, an Ant Colony Optimization based approach useful to solve dynamic vehicle routing problems with time windows, is presented. MACS-DVRPTW is organized with a hierarchy of artificial ant colonies designed to successively optimize a multiple objective function: the first colony minimizes the number of vehicles while the second colony minimizes the traveled distances. Cooperation between c...Show More
A Hierarchical Simple Probabilistic Population-Based Algorithm Applied to the Dynamic TSP
Edward Kupfer;Hoang Thanh Le;Johanna Zitt;Ying-Chi Lin;Martin Middendorf
2021 IEEE Symposium Series on Computational Intelligence (SSCI)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
This work presents a Hierarchical Simple Probabilistic Population-Based algorithm (H-SPPBO) which is a hierarchical version of SPPBO. The SPPBO framework is a general scheme to design population-based algorithms for combinatorial (and other) optimization problems. The proposed H-SPPBO algorithm uses a hierarchical tree structure to make it particularly suitable for dynamic (combinatorial) optimiza...Show More
Solving Shortest Path Problem Using Hopfield Networks and Genetic Algorithms
Matheus Giovanni Pires;Ivan Nunes da Silva;Fabiana Cristina Bertoni
2008 Eighth International Conference on Hybrid Intelligent Systems
Year: 2008 | Conference Paper |
Cited by: Papers (1)
Dynamic programming has provided a powerful approach to optimization problems, but its applicability has been somewhat limited because of the large computational requirements of the standard computational algorithm. In recent years a number of new procedures with reduced computational requirements have been developed. This paper presents a association of a modified Hopfield neural network, which i...Show More
A class of efficiently solvable multistage optimization problems under uncertainty and applications
We investigate a class of specially structured multistage robust optimization problems under uncertainty for which efficient computation of exact optimal robust strategies is possible. As an essential feature of this class of problems, we introduce the concept of a state-space representable uncertainty set, based on an underlying graph-theoretic structure. This concept is shown to extend several p...Show More
Worst case scenario lemma for Γ-robust combinatorial optimization problems under max-min criterion
2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)
Year: 2017 | Conference Paper |
Robust optimization motivated by practical applications deals with optimization problems in which some input parameters are uncertain. In this paper, we consider Γ-robust combinatorial optimization problems under max-min criterion. For this type of problems, we propose and prove a general lemma that we call the worst case scenario lemma; it specifies a worst case scenario for a given solution. Bas...Show More
Dynamic Stochastic Ranking Selection Immune Optimization Algorithm for Dynamical 0/1 Knapsack Problem
2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics
Year: 2013 | Conference Paper |
Cited by: Papers (1)
In this paper, a dynamic stochastic ranking selection immune optimization algorithm with constraints (DSRIOA), Based on adaptive memory and dynamic recognition functions of artificial immune systems, was proposed to deal with knapsack problem with constraints in dynamic environments. A novel dynamic stochastic ranking strategies is used to select excellence antibodies, meanwhile, infeasible antibo...Show More
An Improved Weighted Optimization-based Framework for Large-scale MOPs
2021 IEEE Congress on Evolutionary Computation (CEC)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
This paper proposes an improved weighted optimization-based framework (iWOF) for solving large-scale multiobjective optimization problems (LSMOPs). Compared to the original framework, there are two main contributions in our work. Firstly, a novel evolutionary search strategy involving two different search operators with different search characteristics, i.e., a particle swarm optimization (PSO) op...Show More
Biogeography-based optimization (BBO) cannot effectively solve high-dimensional global optimization problems due to its single migration mechanism and random mutation operator. To get better performance, a dual BBO based on sine cosine algorithm (SCA) and dynamic hybrid mutation is proposed in this work, which named SCBBO. Firstly, the Latin hypercube sampling method is innovatively used to improv...Show More
A dynamic evolutionary multi-objective optimization algorithm based on decomposition and adaptive diversity introduction
2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
Year: 2016 | Conference Paper |
Cited by: Papers (8)
In addition to the need for satisfying several objectives, many problems in the real-world are also time-varying. These dynamic problems need the optimization algorithm to constantly find the time-varying optimal solutions. This paper proposes a dynamic multi-objective evolutionary algorithm based on decomposition and adaptive diversity introduction (denoted by dMOEAD-DI). Specifically, the dMOEAD...Show More
Time-Varying Optimization with Optimal Parametric Functions
2023 62nd IEEE Conference on Decision and Control (CDC)
Year: 2023 | Conference Paper |
In this paper, we consider a formulation of nonlinear constrained optimization problems. We reformulate it as a time-varying optimization using continuous-time parametric functions and derive a dynamical system for tracking the optimal solution. We then re-parameterize the dynamical system to express it based on a linear combination of the parametric functions. Calculus of variations is applied to...Show More
Adaptive particle swarm for solving the Dynamic Vehicle Routing Problem
Mostepha Redouane Khouadjia;Laetitia Jourdan;El-Ghazali Talbi
ACS/IEEE International Conference on Computer Systems and Applications - AICCSA 2010
Year: 2010 | Conference Paper |
Cited by: Papers (13)
Usually, the combinatorial optimization problems are modeled in a static way. All data are known in advance, i.e., before the optimization process has started. But in practice, many problems are dynamic, and change during the time. For the Dynamic Vehicle Routing Problem (DVRP), new orders arrive when the working day plan is in progress. Thus, the routes must be reconfigured dynamically during the...Show More
A constructive search algorithm for combinatorial dynamic optimization problems
2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)
Year: 2015 | Conference Paper |
Cited by: Papers (2)
In most of the optimization studies, the problem related data is assumed to be exactly known beforehand and remain stationary throughout whole optimization process. However, majority of real life problems and their practical applications are dynamic in their nature due to the reasons arising from unpredictable events, such as rush orders, fluctuating capacities of manufacturing constraints, change...Show More
A fast evolutionary algorithm for dynamic bi-objective optimization problems
2012 7th International Conference on Computer Science & Education (ICCSE)
Year: 2012 | Conference Paper |
Cited by: Papers (3)
Many real-world optimization problems involve multiple objectives, constraints, and parameters which constantly change with time. In this paper, we suggest a fast dynamic bi-objective evolutionary algorithm (DBOEA). Specifically, a fast bi-objective non-dominated sorting is introduced to reduce the cost of the layering of non-dominated fronts. A differential evolution operator is also adopted as t...Show More
Multiobjective evolutionary algorithm for dynamic nonlinear constrained optimization problems
A new method to solve dynamic nonlinear constrained optimization problems (DNCOP) is proposed. First, the time (environment) variable period of DNCOP is divided into several equal subperiods. In each subperiod, the DNCOP is approximated by a static nonlinear constrained optimization problem (SNCOP). Second, for each SNCOP, inspired by the idea of multiobjective optimization, it is transformed into...Show More
IEEE Account
Purchase Details
Profile Information
Need Help?
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Contact Us
- Help
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Sitemap
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.