Results 21 to 30 of about 2,657,297 (246)

Meta-heuristic F1—F7.

, 2023
The numerical results of the proposed algorithm and the eight meta-heuristic algorithms are given for the optimization of the unimodal benchmark test functions of F1-F7. (PDF)
Yong Gao (29929), Huaifeng Zhang (14341541), Yingying Duan (1700185), Hao Zhang (15339)   +3 more
core   +1 more source

Meta-heuristic F8—F13.

, 2023
The numerical results of the proposed algorithm and the eight meta-heuristic algorithms are given for the optimization of the multimodal benchmark test functions of F8-F13. (PDF)
Yong Gao (29929), Huaifeng Zhang (14341541), Yingying Duan (1700185), Hao Zhang (15339)   +3 more
core   +1 more source

Meta-heuristic F14—F21.

, 2023
The numerical results of the proposed algorithm and the eight meta-heuristic algorithms are given for the optimization of the others benchmark test functions of F14-F21. (PDF)
Yong Gao (29929), Huaifeng Zhang (14341541), Yingying Duan (1700185), Hao Zhang (15339)   +3 more
core   +1 more source

A Meta-Heuristic Load Balancer for Cloud Computing Systems [PDF]

, 2015
This paper introduces a strategy to allocate services on a cloud system without overloading the nodes and maintaining the system stability with minimum cost.
Getov, Vladimir, Leszek Sliwko, Vladimir Getov, Sliwko, L.   +3 more
core   +1 more source

Dynamic Group-Based Cooperative Optimization Algorithm

IEEE Access, 2020
Several optimization problems from various types of applications have been efficiently resolved using available meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithm.
Mohamad M. Fouad, Ali Ibrahim El-Desouky, Rami Al-Hajj, El-Sayed M. El-Kenawy   +3 more
doaj   +1 more source

Surrogate-Assisted Hybrid Meta-Heuristic Algorithm with an Add-Point Strategy for a Wireless Sensor Network

Entropy, 2023
Meta-heuristic algorithms are widely used in complex problems that cannot be solved by traditional computing methods due to their powerful optimization capabilities. However, for high-complexity problems, the fitness function evaluation may take hours or
Jeng-Shyang Pan, Li-Gang Zhang, Shu-Chuan Chu, Chin-Shiuh Shieh, Junzo Watada   +4 more
doaj   +1 more source

Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application

, 2022
In recent years, the increasing use of the Internet of Things (IoT) has generated excessive amounts of data. It is difficult to manage and control the volume of data used in cloud computing, and since cloud computing has problems with latency, lack of ...
Dadmehr Rahbari
core   +1 more source

A Meta-Heuristic-Based Approach for Qos-Aware Service Composition

IEEE Access, 2020
Recently, with the rapid increase in the number of web services, QoS-aware Web Service Composition(QWSC) has become a popular topic in both industry and academia. Meta-heuristic algorithm, as an effective way to solve classical optimization problems, has
Chenyang Li, Jun Li, Huiling Chen
doaj   +1 more source

Nature-inspired optimization algorithms in knapsack problem: A review [PDF]

المجلة العراقية للعلوم الاحصائية, 2019
Meta-heuristic algorithms have become an arising field of research in recent years. Some of these algorithms have proved to be efficient in solving combinatorial optimization problems, particularly knapsack problem.
Ghalya Tawfeeq Basheer, Zakariya Algamal
doaj   +1 more source

Agent State Flipping Based Hybridization of Heuristic Optimization Algorithms: A Case of Bat Algorithm and Krill Herd Hybrid Algorithm

Algorithms, 2021
This paper describes a unique meta-heuristic technique for hybridizing bio-inspired heuristic algorithms. The technique is based on altering the state of agents using a logistic probability function that is dependent on an agent’s fitness rank.
Robertas Damaševičius, Rytis Maskeliūnas   +1 more
doaj   +1 more source