Results 81 to 90 of about 3,843 (214)

A multi-objective metamodel-assisted memetic algorithm with strength-based local refinement

, 2009
International audienceMetamodel-Assisted Evolutionary Algorithms are low-cost optimization methods for CPU demanding problems. Memetic Algorithms combine global and local search methods, aiming at improving the quality of promising solutions.
Giannakoglou, KC, Georgopoulou, CA, Giannakoglou, Kyriakos C., Georgopoulou, Chariklia   +3 more
core   +1 more source

Agent-based Optimization of Advisory Strategy Parameters

Journal of Telecommunications and Information Technology, 2013
In this paper, an application of Evolutionary Multiagent Systems (EMAS) and its memetic version to the optimization of advisory strategy (in this case, Sudoku advisory strategy) is considered.
Mateusz Polnik, Mateusz Kumięga, Aleksander Byrski   +2 more
doaj   +1 more source

Energy‐aware flexible job shop scheduling problem with nonlinear routes and position‐based learning effect

International Transactions in Operational Research, Volume 33, Issue 2, Page 860-891, March 2026.
Abstract Sustainability has become one of the main objectives in all human activities and, in particular, in manufacturing environments. In this paper, we consider the flexible job shop scheduling problem with the objective of minimizing energy consumption.
Ernesto G. Birgin, José Angel Riveaux, Débora P. Ronconi   +2 more
wiley   +1 more source

Memetic algorithm flow chart.

, 2018
Memetic algorithm flow chart.
Safa M. Gasser (4915471), Mohamed S. El-Mahallawy (4915465), Karim Hammad (4915468), Ahmed M. El Bakly (4915462), Rahab M. Ramadan (4915474)   +4 more
core   +1 more source

A Hybrid Framework for Stock Price Forecasting Using Metaheuristic Feature Selection Approaches and Transformer Models Enhanced by Temporal Embedding and Attention Pruning

Applied AI Letters, Volume 7, Issue 1, February 2026.
Workflow of the proposed hybrid BWO‐Transformer framework for stock price prediction. ABSTRACT Accurately predicting stock prices remains a major challenge in financial analytics due to the complexity and noise inherent in market data. Feature selection plays a critical role in improving both computational efficiency and predictive performance. In this
Amirhossein Malakouti Semnani, Sohrab Kordrostami, Amirhossein Refahi Sheikhani, Mohammad Hossein Moattar   +3 more
wiley   +1 more source

A Computationally Efficient Stochastic Method for Quantifying the Effects of Multi‐Surrogate Model Uncertainty on Saltwater Remediation Optimization

Water Resources Research, Volume 62, Issue 1, January 2026.
Abstract Machine learning models are highly potential to substitute computationally intensive numerical simulation models in saltwater intrusion (SWI) remediation optimization. However, uncertainty inherent in machine learning models can propagate through predictions into optimization, resulting in inaccurate solutions.
Yulu Huang, Jina Yin, Chunhui Lu
wiley   +1 more source

Memetic Algorithms with Local Search Chains in R: The Rmalschains Package

Journal of Statistical Software, 2016
Global optimization is an important field of research both in mathematics and computer sciences. It has applications in nearly all fields of modern science and engineering.
Christoph Bergmeir, Daniel Molina, José M. Benítez   +2 more
doaj   +1 more source

Reinforcement Learning‐Assisted Meta‐Heuristics for Scheduling Job Shops With Material Handling Robots

IET Collaborative Intelligent Manufacturing, Volume 8, Issue 1, January/December 2026.
This study addresses an integrated job shop scheduling problem with material handling robots, aiming to minimise the maximum completion time. Three meta‐heuristics, seven local search strategies and two reinforcement learning algorithms are proposed to solve the problems.
Qi Jia, Kaizhou Gao, Naiqi Wu, Ponnuthurai Nagaratnam Suganthan   +3 more
wiley   +1 more source

Advances in Hybrid Evolutionary Computation for Continuous Optimization

, 2011
Evolutionary Algorithms (EAs) are a set of optimization techniques that have become highly popular in recent decades. One of the main reasons for this success is that they provide a general purpose mechanism for solving a wide range of problems.
Muelas Pascual, Santiago
core   +1 more source

Memetic Algorithm With Meta-Lamarckian Learning and Simplex Search for Distributed Flexible Assembly Permutation Flowshop Scheduling Problem

IEEE Access, 2020
This paper studies a novel and practical distributed flexible assembly permutation flowshop scheduling problem with makespan criterion, which has attracted wide attention due to important applications in modern manufacturing.
Guanghui Zhang, Keyi Xing, Guangyun Zhang, Zhenxue He   +3 more
doaj   +1 more source