concept

global optimization

Also known as: global optimization problems

Facts (11)

Sources

A comprehensive overview on demand side energy management ... link.springer.com Springer Mar 13, 2023 6 facts

referenceZhang L, Liu L, Yang X-S, and Dai Y published 'A novel hybrid firefly algorithm for global optimization' in PLoS ONE, volume 11, issue 9, article e0163230, in 2016.

referenceMitras and Sultan (2013) presented a novel hybrid imperialist competitive algorithm for global optimization in the Australian Journal of Basic and Applied Sciences.

referenceKuo H and Lin C (2013) introduced a cultural evolution algorithm designed for global optimization problems and discussed its applications.

procedureWhen choosing an algorithm to solve demand side management optimization issues, factors such as problem type (single- or multi-objective), optimization type (local or global), robustness, and accuracy must be considered.

referenceKuo H and Lin C (2013) developed a cultural evolution algorithm for global optimization problems and explored its applications.

referenceZhang L, Liu L, Yang X-S, and Dai Y published 'A novel hybrid firefly algorithm for global optimization' in the journal PLoS ONE in 2016.

Demand side management using optimization strategies for efficient ... journals.plos.org PLOS ONE Mar 21, 2024 5 facts

claimThe Chaotic Harris Hawks Optimization (CHHO) algorithm is appreciated for its adaptive search capability in global optimization challenges for demand-side management.

referenceAbdollahzadeh B., Gharehchopogh F. S., and Mirjalili S. introduced the 'African vultures optimization algorithm' as a nature-inspired metaheuristic for global optimization problems in a 2021 paper in Computers & Industrial Engineering.

claimThe Chaotic Improved Artificial Swarm (CIAS) algorithm is valued for its interactive learning approach in global optimization challenges for demand-side management.

claimThe Cuckoo Search (CS) algorithm is recognized for its fast convergence in global optimization challenges for demand-side management.

claimThe Slime Mould Algorithm (SMA) is distinguished for its ability to avoid local optima in global optimization challenges for demand-side management.