Book ChapterDOI
Heuristic and Meta-Heuristic Optimization
Prajna Kunche,K. V. V. S. Reddy +1 more
- pp 17-24
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This chapter deals with the fundamentals of the optimization, and it also presents various existing heuristic and meta-heuristic optimization techniques.Abstract:
This chapter deals with the fundamentals of the optimization. The concepts of stochastic optimization and how the stochastic optimization is advantageous over the deterministic approaches are described in Sect. 3.2. Heuristic and meta-heuristic optimization techniques are defined in Sect. 3.3, and it also presents various existing heuristic and meta-heuristic optimization techniques. The fundamentals of the swarm intelligence are given in Sect. 3.4. The applications of the swarm intelligence in various fields are also presented in this section.read more
Citations
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Journal ArticleDOI
A review of different optimisation techniques for solving single and multi-objective optimisation problem in power system and mostly unit commitment problem
D. V. N. Ananth,K. S. T. Vineela +1 more
TL;DR: This paper presents a meta-modelling system that automates the very labor-intensive and therefore time-heavy and expensive and therefore expensive and expensive operation of generator units by automating their construction and maintenance.
Journal ArticleDOI
Analysis of Constraint-Handling in Metaheuristic Approaches for the Generation and Transmission Expansion Planning Problem with Renewable Energy
Lourdes Martínez-Villaseñor,Hiram Ponce,Jose Antonio Marmolejo-Saucedo,Juan M. Ramirez,Agustina Hernández +4 more
TL;DR: This work proposed to address the G&TEP problem with a pure genetic algorithm approach based on a centralized planned transmission expansion, and different constraint-handling techniques were applied to deal with two complex case studies presented.
Journal ArticleDOI
ODM-BCSA: An Offloading Decision-Making Framework based on Binary Cuckoo Search Algorithm for Mobile Edge Computing
Journal ArticleDOI
A Survey of Computational Offloading in Cloud/Edge-based Architectures: Strategies, Optimization Models and Challenges
TL;DR: In this article, different offloading models are examined to identify the offloading parameters that need to be optimized, and compared several optimization techniques used to optimize offloading decisions, specifically Swarm Intelligence (SI) models, since they are best suited to the distributed aspect of edge computing.
References
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Journal ArticleDOI
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Journal ArticleDOI
A simplex method for function minimization
John A. Nelder,R. Mead +1 more
TL;DR: A method is described for the minimization of a function of n variables, which depends on the comparison of function values at the (n 41) vertices of a general simplex, followed by the replacement of the vertex with the highest value by another point.
Book
Numerical Optimization
Jorge Nocedal,Stephen J. Wright +1 more
TL;DR: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization, responding to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems.
Book
Artificial Intelligence: A Modern Approach
Stuart Russell,Peter Norvig +1 more
TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.