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Other Important Optimization Algorithms
Published in Krishn Kumar Mishra, Nature-Inspired Algorithms, 2023
In addition to these point-to-point-based algorithms, population-based algorithms are also used for solving optimization problems. Population-based algorithms start their search with solutions and implement some natural phenomena to generate new solutions. Some population-based algorithms are designed by mapping the intelligence of swarms in a program. These algorithms are called swarm intelligence–based algorithms. Some popular swarm intelligence algorithms are particle swarm optimization and grey wolf optimization. We discuss the applications of these algorithms later in this book. Other swarm intelligence algorithms are ACO, ant–bee colony, whale optimization algorithms, bat optimization algorithms, cuckoo search algorithms, firefly algorithms, and ant–lion optimization algorithms. In this chapter, we give a brief overview of some of them.
Swarm Intelligence Techniques for Optimizing Problems
Published in Anand Nayyar, Dac-Nhuong Le, Nhu Gia Nguyen, Advances in Swarm Intelligence for Optimizing Problems in Computer Science, 2018
Swarm intelligence is observed from nature and is based on the collective efforts of simple creatures like ants, bees, and termites. Massive heaps are built by termites. Bees build enormous stores effectively without loss. The modern applications inspired by the swarms of the bees building the hives can be used by the data networking technology. The knowledge obtained from thousands of very simple devices can be aggregated. The summation of the data can benefit specific pieces of the apparatus. The system can report problems in the network as the whole, and it can have future relevance to the Internet of Things. IoT is a network of simple devices and generates huge data; the collective effort of every individual in the network can address many challenges in terms of intelligence (Sood, Sandhu, Singla, & Chang, 2017). Figure 9.4. represents the architecture of the Internet of Things.
Intelligent Agents in Telecommunication Networks
Published in Witold Pedrycz, Athanasios Vasilakos, Computational Intelligence in Telecommunications Networks, 2018
Costas Tsatsoulis, Leen-Kiat Soh
In this section, we present several agent technologies in telecommunication networks and mention several projects and research activities that employ intelligent agents in network management architecture, network diagnosis, traffic control and routing, network mobility platform, network configuration, and network monitoring and accounting. These technologies include mobile agents or mobile computing, intelligent interface between agent and human users, swarm intelligence, and economic modeling. Mobile agents play an important role in spreading intelligence across networks when they travel. The mobility allows them to be created, deployed, and terminated without disrupting the network configuration. Interface agents model human managers and learn from them how to manage networks. This area of research has not been applied to telecommunication networks directly, but has the potential to automate or assist in the tasks of system administration and network management. Swarm intelligence stems from the work of artificial life in which unintelligent agents work independently or with relative small amount of collaboration to achieve a greater goal that requires intelligence. Then, we briefly touch on designing network management systems after economic models.
Predicting the carbon dioxide emission of China using a novel augmented hypo-variance brain storm optimisation and the impulse response function
Published in Environmental Technology, 2021
Kofi Baah Boamah, Jianguo Du, Daniel Adu, Claudia Nyarko Mensah, Lamini Dauda, Muhammad Aamir Shafique Khan
The Part II presents studies that employed the swarm intelligence algorithms. The most frequently used swarm intelligence optimisation algorithms include Particle Swarm Optimization (PSO), Bee Colony, Ant Colony Optimisation, and Bat Algorithms. The swarm optimisation is postulated to provide key decisions involving complex computational problems in varied fields. The underlying concepts for the modelling and simulating the results is based on the actions of birds. Just as birds flying to a new habitat, leads others to also fly to the new habitat with each bird learning from others for the best position, uniquely without any collision, the PSO mimic such action of birds looking for the best habitat. The Particle Swarm optimisation is based on swarm intelligence, hence capable of optimising for the best search solution based on individual cooperation and/or the competition in a group.
A Control Architecture for Robot Swarms (AMEB)
Published in Cybernetics and Systems, 2019
Angel Gil, Jose Aguilar, Eladio Dapena, Rafael Rivas
Swarm intelligence (SI) seeks to explain natural or artificial systems formed by multiple individuals where coordination occurs in a decentralized way (Aguilar et al. 2013).The SI emphasizes in the collective behaviors that emerge as a result of local interactions between individuals and with the environment; a fundamental characteristic is that the system acts in a coordinated way if there is a central entity that controls them. The coordination is more complex, if there is not a central entity for this task. Robot swarms can be defined as an application of swarm intelligence for the control of multi-robot systems (Dorigo 2009), formed by a high number of homogeneous robotic agents, with simple individual characteristics, that when working together can get to execute tasks of high complexity (Ahmed and Glasgow 2012). In this paper, we propose an architecture for the management of robot swarms, in order to allow the emergence and self-organized behavior on the swarm.
On the performance analysis of solving the Rubik’s cube using swarm intelligence algorithms
Published in Applied Artificial Intelligence, 2022
Swarm intelligent systems consist of a population of agents that follow simple rules to interact with each other and their environment. These interactions can sometimes seem random when we observe the behavior of each agent individually. This varying local behaviour leads to intelligent global behaviour, unknown to the individual agents. Some examples are ant colonies, flocking of birds, hunting patterns of hawks, herding behaviour of animals, bacterial growth, fish schooling, and intelligent microbial organisms. Swarm intelligence algorithms are popular for solving many problems because they are cheap, robust, and easy to implement.