Javidan Kazemi Kordestani

Orcid: 0000-0002-9806-3178

According to our database1, Javidan Kazemi Kordestani authored at least 14 papers between 2014 and 2021.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2021
Advances in Learning Automata and Intelligent Optimization
Intelligent Systems Reference Library 208, Springer, ISBN: 978-3-030-76290-2, 2021

A Two-Level Function Evaluation Management Model for Multi-Population Methods in Dynamic Environments: Hierarchical Learning Automata Approach.
J. Exp. Theor. Artif. Intell., 2021

2020
A note on the exclusion operator in multi-swarm PSO algorithms for dynamic environments.
Connect. Sci., 2020

2019
A novel framework for improving multi-population algorithms for dynamic optimization problems: A scheduling approach.
Swarm Evol. Comput., 2019

New measures for comparing optimization algorithms on dynamic optimization problems.
Nat. Comput., 2019

2018
Cellular teaching-learning-based optimization approach for dynamic multi-objective problems.
Knowl. Based Syst., 2018

Self-adaptive multi-population genetic algorithms for dynamic resource allocation in shared hosting platforms.
Genet. Program. Evolvable Mach., 2018

An adaptive bi-flight cuckoo search with variable nests for continuous dynamic optimization problems.
Appl. Intell., 2018

2016
An efficient oscillating inertia weight of particle swarm optimisation for tracking optima in dynamic environments.
J. Exp. Theor. Artif. Intell., 2016

2015
LADE: Learning Automata Based Differential Evolution.
Int. J. Artif. Intell. Tools, 2015

A novel hybrid adaptive collaborative approach based on particle swarm optimization and local search for dynamic optimization problems.
Appl. Soft Comput., 2015

2014
A note on the paper "A multi-population harmony search algorithm with external archive for dynamic optimization problems" by Turky and Abdullah.
Inf. Sci., 2014

CDEPSO: a bi-population hybrid approach for dynamic optimization problems.
Appl. Intell., 2014

An improved Differential Evolution algorithm using learning automata and population topologies.
Appl. Intell., 2014


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