Penalty Function Methods for Constrained Optimization with Genetic Algorithms
Reads0
Chats0
TLDR
These penalty-based methods for handling constraints in Genetic Algorithms are presented and discussed and their strengths and weaknesses are discussed.Abstract:
Genetic Algorithms are most directly suited to unconstrained optimization. Application of Genetic Algorithms to constrained optimization problems is often a challenging effort. Several methods have been proposed for handling constraints. The most common method in Genetic Algorithms to handle constraints is to use penalty functions. In this paper, we present these penalty-based methods and discuss their strengths and weaknesses.read more
Citations
More filters
Journal ArticleDOI
A Novel Multi-Robot Task Allocation Model in Marine Plastics Cleaning Based on Replicator Dynamics
Le Hong,Weicheng Cui,Hao Chen +2 more
TL;DR: A novel multi-robot task allocation (MRTA) model based on replicator dynamics for marine plastic cleaning that not only satisfies the minimization of the cost function, but also reaches a relatively stable state of the task allocation.
Journal ArticleDOI
Fitness inheritance-based evolutionary algorithm and its application in hybrid electric vehicle design
Li Zhao,Wan-Ke Cao,Yu-Tao He +2 more
TL;DR: The experimental results show that the FIEA algorithm is a powerful tool in optimising a parallel HEV and FC and the emissions can be improved clearly while the performance of the vehicle is not sacrificed.
Journal ArticleDOI
Impact of Optimal Control of Distributed Generation Converters in Smart Transformer Based Meshed Hybrid Distribution Network
TL;DR: In this paper, a smart transformer based meshed hybrid distribution network is realized by extending ST low voltage dc (LVDC) link to form a LVDC line which connects dc buses of existing distributed generation (DG) converters.
Journal ArticleDOI
Sensor-Less Predictive Drying Control of Pneumatic Conveying Batch Dryers
TL;DR: An innovative control oriented model of the dryer is derived from first principle’s encompassing a soft sensor-based online powder-moisture measurement procedure replacing the physical moisture sensors.
Proceedings ArticleDOI
Hybrid PSO Algorithm with Iterated Local Search Operator for Equality Constraints Problems
TL;DR: A hybrid PSO algorithm with an ILS (Iterated Local Search) operator for handling equality constraints problems in mono-objective optimization problems and shows improvement in accuracy, reducing the gap for the tested problems.
References
More filters
Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
Genetic Algorithms + Data Structures = Evolution Programs
TL;DR: GAs and Evolution Programs for Various Discrete Problems, a Hierarchy of Evolution Programs and Heuristics, and Conclusions.
Book
Nonlinear Programming: Theory and Algorithms
TL;DR: The book is a solid reference for professionals as well as a useful text for students in the fields of operations research, management science, industrial engineering, applied mathematics, and also in engineering disciplines that deal with analytical optimization techniques.
Journal ArticleDOI
An efficient constraint handling method for genetic algorithms
TL;DR: GA's population-based approach and ability to make pair-wise comparison in tournament selection operator are exploited to devise a penalty function approach that does not require any penalty parameter to guide the search towards the constrained optimum.
Book
Evolutionary algorithms in theory and practice
TL;DR: In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming within a unified framework, thereby clarifying the similarities and differences of these methods.