A comprehensive survey on particle swarm optimization algorithm and its applications
Particle swarm optimization (PSO) is a heuristic global optimization method, proposed
originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used …
originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used …
Lifecycle model of a negotiation agent: A survey of automated negotiation techniques
Negotiation is a complex process. The decision making involved in several stages of
negotiation makes its automation complex. In this paper we present a lifecycle model of a …
negotiation makes its automation complex. In this paper we present a lifecycle model of a …
A new multi-agent particle swarm algorithm based on birds accents for the 3D indoor deployment problem
The 3D indoor deployment of sensor nodes is a complex real world problem, proven to be
NP-hard and difficult to resolve using classical methods. In this context, we propose a hybrid …
NP-hard and difficult to resolve using classical methods. In this context, we propose a hybrid …
Concurrent bilateral negotiation for open e-markets: the Conan strategy
We develop a novel strategy that supports software agents to make decisions on how to
negotiate for a resource in open and dynamic e-markets. Although existing negotiation …
negotiate for a resource in open and dynamic e-markets. Although existing negotiation …
What to bid and when to stop
T Baarslag - 2014 - eprints.soton.ac.uk
Negotiation is an important activity in human society, and is studied by various disciplines,
ranging from economics and game theory, to electronic commerce, social psychology, and …
ranging from economics and game theory, to electronic commerce, social psychology, and …
A high-order norm-product regularized multiple kernel learning framework for kernel optimization
Kernels offer an effective alternative to implicitly embed the original data into a higher or
infinite-dimensional space in support vector machines. Kernel learning, which attempts to …
infinite-dimensional space in support vector machines. Kernel learning, which attempts to …
A consistency and consensus-driven approach for granulating linguistic information in GDM with distributed linguistic preference relations
H Su, Q Wu, X Tang, T Huang - Artificial Intelligence Review, 2023 - Springer
This study considers the linguistic information granulation in GDM scenarios where both the
outcomes of pairwise comparisons coming from decision makers (DMs) over alternatives …
outcomes of pairwise comparisons coming from decision makers (DMs) over alternatives …
Graph-Driven Resource Allocation Strategies in Satellite IoT: A Cooperative Game Theoretic Approach
H Fan, C Sun, J Long, L Li, Y Huo… - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
Multi-satellite intelligent computing, aimed at autonomously generating resource allocation
schemes without reliance on ground control, is a crucial technology for the intelligent and …
schemes without reliance on ground control, is a crucial technology for the intelligent and …
A proactive inference scheme for data-aware decision making in support of pervasive applications
K Kolomvatsos - Future Generation Computer Systems, 2022 - Elsevier
The advent of the Internet of Things (IoT) offers a huge infrastructure where numerous
devices can collect and process data retrieved by their environment. Due to the limited …
devices can collect and process data retrieved by their environment. Due to the limited …
Optimizing resource allocation using multi-objective particle swarm optimization in cloud computing systems
E Alkayal - 2018 - eprints.soton.ac.uk
Allocating resources in data centers is a complex task due to their increase in size,
complexity, and consumption of power. At the same time, consumers' requirements …
complexity, and consumption of power. At the same time, consumers' requirements …