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Culture based particle swarm optimization framework for constrained dynamic multiple objective optimization.

机译：基于文化的粒子群优化框架，用于约束动态多目标优化。

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摘要

Real world optimization problems are usually dynamic multiple objective in nature with several constraints on the function values in the presence of uncertainties and bounds on the inputs. This work proposes systematic segmentation of the said problem using Cultural Algorithms. This is achieved by maintaining feasible and infeasible best individuals and their fitnesses and constraint violations in the Situational Space, bounds for the search in the Normative Space, crowding information in the Topographic Space, and function sensitivity and relocation offsets in Historical knowledge space of the Cultural Algorithm. The information is used to vary the flight parameters of the PSO equations, to generate newer individuals and to track dynamic multiple objective optima with constraints.

机译：现实世界中的优化问题通常本质上是动态的多目标问题，在输入存在不确定性和界限的情况下，对函数值有一些约束。这项工作提出使用文化算法对该问题进行系统的分割。这是通过在情境空间中保持可行和不可行的最佳个体及其适应度和约束违规，在规范空间中进行搜索的范围，在地形空间中拥挤信息以及在文化领域的历史知识空间中进行功能敏感性和重新定位偏移来实现的。算法。该信息用于更改PSO方程的飞行参数，生成更新的个体并跟踪带有约束的动态多目标最优。

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作者
Kadkol, Ashwin A.;
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作者单位

Oklahoma State University.;

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授予单位 Oklahoma State University.;
学科 Engineering Computer.;Engineering Electronics and Electrical.
学位 M.S.
年度 2010
页码 118 p.
总页数 118
原文格式 PDF
正文语种 eng
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专利

1. A Culture-Based Particle Swarm Optimization Framework for Dynamic, Constrained Multi-Objective Optimization [J] . Ashwin A. Kadkol, Gary G. Yen International journal of swarm intelligence research . 2012,第1期

机译：基于文化的粒子群优化框架，用于动态，受约束的多目标优化
2. A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization [J] . Zain Mohamad Zihin bin Mohd, Kanesan Jeevan, Chuah Joon Huang, Applied Soft Computing . 2018,第期

机译：基于动态边界搜索的多目标粒子群优化算法对约束优化
3. Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization. [J] . Liu ShuangYin, Xu LongQin, Li DaoLiang, Computers and Electronics in Agriculture . 2013,第Null期

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4. Particle Swarm Optimization with feasibility rules in constrained numerical optimization. A brief review [C] . Vicente-Josué Aguilera-Rueda, María-Yaneli Ameca-Alducin, Efrén Mezura-Montes, IEEE International Meeting on Power, Electronics and Computing . 2016

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5. Optimization-based mechanism synthesis using multi-objective parallel asynchronous particle swarm optimization. [D] . McDougall, Robin. 2008

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6. Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism [O] . Wusi Yang, Li Chen, Yi Wang, 2020

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7. Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization. [O] . Maryam Mousavi, Hwa Jen Yap, Siti Nurmaya Musa, 2017

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8. Minimum Fuel Trajectory Design in Multiple Dynamical Environments Utilizing Direct Transcription Methods and Particle Swarm Optimization. [R] . Zurita, A. J. G. 2016

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Culture based particle swarm optimization framework for constrained dynamic multiple objective optimization.

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