Understanding the limitations of particle swarm algorithm for dynamic optimization tasks:A survey towards the singularity of PSO for swarm robotic applications

One of the most widely used biomimicry algorithms is the Particle Swarm Optimization (PSO). Since its introduction in 1995, it has caught the attention of both researchers and academicians as a way of solving various optimization problems, such as in the fields of engineering and medicine, to computer image processing and mission critical operations. PSO has been widely applied in the field of swarm robotics, however, the trend of creating a new variant PSO for each swarm robotic project is alarming. We investigate the basic properties of PSO algorithms relevant to the implementation of swarm robotics and characterize the limitations that promote this trend to manifest. Experiments were conducted to investigate the convergence properties of three PSO variants (original PSO, SPSO and APSO) and the global optimum and local optimal of these PSO algorithms were determined. We were able to validate the existence of premature convergence in these PSO variants by comparing 16 functions implemented alongside the PSO variant. This highlighted the fundamental flaws in most variant PSOs, and signifies the importance of developing a more generalized PSO algorithm to support the implementation of swarm robotics. This is critical in curbing the influx of custom PSO and theoretically addresses the fundamental flaws of the existing PSO algorithm.</jats:p

PSO Algorithm Based Resource Allocation for OFDM Cognitive Radio

With the development of remote correspondences, the issue of data transmission lack has turned out to be more conspicuous. Then again, to sense the presence of authorized clients, range detecting procedures are utilized. Vitality recognition, Matched channel identification and Cyclo-stationary component location are the three ordinary techniques utilized for range detecting. However there are a few downsides of these strategies. The execution of vitality indicator is helpless to instability in noise power. Coordinated channel range detecting strategies require a devoted collector for each essential client. Cyclo-stationary element Detection requires parcel of calculation exertion and long perception time. This proposition talks about the routine vitality location strategy and proposed enhanced vitality identification technique utilizing cubing operation. Additionally, cyclic prefix based range detecting is talked about in this theory. Scientific Description of vitality location and cyclic prefix based range detecting strategies is likewise delineated for fading channels

Treasure hunt : a framework for cooperative, distributed parallel optimization

Orientador: Prof. Dr. Daniel WeingaertnerCoorientadora: Profa. Dra. Myriam Regattieri DelgadoTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 27/05/2019Inclui referências: p. 18-20Área de concentração: Ciência da ComputaçãoResumo: Este trabalho propõe um framework multinível chamado Treasure Hunt, que é capaz de distribuir algoritmos de busca independentes para um grande número de nós de processamento. Com o objetivo de obter uma convergência conjunta entre os nós, este framework propõe um mecanismo de direcionamento que controla suavemente a cooperação entre múltiplas instâncias independentes do Treasure Hunt. A topologia em árvore proposta pelo Treasure Hunt garante a rápida propagação da informação pelos nós, ao mesmo tempo em que provê simutaneamente explorações (pelos nós-pai) e intensificações (pelos nós-filho), em vários níveis de granularidade, independentemente do número de nós na árvore. O Treasure Hunt tem boa tolerância à falhas e está parcialmente preparado para uma total tolerância à falhas. Como parte dos métodos desenvolvidos durante este trabalho, um método automatizado de Particionamento Iterativo foi proposto para controlar o balanceamento entre explorações e intensificações ao longo da busca. Uma Modelagem de Estabilização de Convergência para operar em modo Online também foi proposto, com o objetivo de encontrar pontos de parada com bom custo/benefício para os algoritmos de otimização que executam dentro das instâncias do Treasure Hunt. Experimentos em benchmarks clássicos, aleatórios e de competição, de vários tamanhos e complexidades, usando os algoritmos de busca PSO, DE e CCPSO2, mostram que o Treasure Hunt melhora as características inerentes destes algoritmos de busca. O Treasure Hunt faz com que os algoritmos de baixa performance se tornem comparáveis aos de boa performance, e os algoritmos de boa performance possam estender seus limites até problemas maiores. Experimentos distribuindo instâncias do Treasure Hunt, em uma rede cooperativa de até 160 processos, demonstram a escalabilidade robusta do framework, apresentando melhoras nos resultados mesmo quando o tempo de processamento é fixado (wall-clock) para todas as instâncias distribuídas do Treasure Hunt. Resultados demonstram que o mecanismo de amostragem fornecido pelo Treasure Hunt, aliado à maior cooperação entre as múltiplas populações em evolução, reduzem a necessidade de grandes populações e de algoritmos de busca complexos. Isto é especialmente importante em problemas de mundo real que possuem funções de fitness muito custosas. Palavras-chave: Inteligência artificial. Métodos de otimização. Algoritmos distribuídos. Modelagem de convergência. Alta dimensionalidade.Abstract: This work proposes a multilevel framework called Treasure Hunt, which is capable of distributing independent search algorithms to a large number of processing nodes. Aiming to obtain joint convergences between working nodes, Treasure Hunt proposes a driving mechanism that smoothly controls the cooperation between the multiple independent Treasure Hunt instances. The tree topology proposed by Treasure Hunt ensures quick propagation of information, while providing simultaneous explorations (by parents) and exploitations (by children), on several levels of granularity, regardless the number of nodes in the tree. Treasure Hunt has good fault tolerance and is partially prepared to full fault tolerance. As part of the methods developed during this work, an automated Iterative Partitioning method is proposed to control the balance between exploration and exploitation as the search progress. A Convergence Stabilization Modeling to operate in Online mode is also proposed, aiming to find good cost/benefit stopping points for the optimization algorithms running within the Treasure Hunt instances. Experiments on classic, random and competition benchmarks of various sizes and complexities, using the search algorithms PSO, DE and CCPSO2, show that Treasure Hunt boosts the inherent characteristics of these search algorithms. Treasure Hunt makes algorithms with poor performances to become comparable to good ones, and algorithms with good performances to be capable of extending their limits to larger problems. Experiments distributing Treasure Hunt instances in a cooperative network up to 160 processes show the robust scaling of the framework, presenting improved results even when fixing a wall-clock time for the instances. Results show that the sampling mechanism provided by Treasure Hunt, allied to the increased cooperation between multiple evolving populations, reduce the need for large population sizes and complex search algorithms. This is specially important on real-world problems with time-consuming fitness functions. Keywords: Artificial intelligence. Optimization methods. Distributed algorithms. Convergence modeling. High dimensionality

Mobile Robots

The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations

Dynamic Resource Allocation Algorithms for Cognitive Radio Systems

Cognitive Radio (CR) is a novel concept for improving the utilization of the radio spectrum. This promises the efficient use of scarce radio resources. Orthogonal Frequency Division Multiplexing (OFDM) is a reliable transmission scheme for Cognitive Radio Systems which provides flexibility in allocating the radio resources in dynamic environment. It also assures no mutual interference among the CR radio channels which are just adjacent to each other. Allocation of radio resources dynamically is a major challenge in cognitive radio systems. In this project, various algorithms for resource allocation in OFDM based CR systems have been studied. The algorithms attempt to maximize the total throughput of the CR system (secondary users) subject to the total power constraint of the CR system and tolerable interference from and to the licensed band (primary users). We have implemented two algorithms Particle Swarm Algorithm(PSO) and Genetic Algorithm(GA) and compared their results

A Survey of Algorithms, Applications and Trends for Particle Swarm Optimization

Particle swarm optimization (PSO) is a popular heuristic method, which is capable of effectively dealing with various optimization problems. A detailed overview of the original PSO and some PSO variant algorithms is presented in this paper. An up-to-date review is provided on the development of PSO variants, which include four types i.e., the adjustment of control parameters, the newly-designed updating strategies, the topological structures, and the hybridization with other optimization algorithms. A general overview of some selected applications (e.g., robotics, energy systems, power systems, and data analytics) of the PSO algorithms is also given. In this paper, some possible future research topics of the PSO algorithms are also introduced.This research received no external funding

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 optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Multi-agent control and operation of electric power distribution systems

This dissertation presents operation and control strategies for electric power distribution systems containing distributed generators. First, models of microturbines and fuel cells are developed. These dynamic models are incorporated in a power system analysis package. Second, operation of these generators in a distribution system is addressed and load following schemes are designed. The penetration of distributed generators (DGs) into the power distribution system stability becomes an issue and so the control of those DGs becomes necessary. A decentralized control structure based on conventional controllers is designed for distributed generators using a new developed optimization technique called Guided Particle Swarm Optimization. However, the limitations of the conventional controllers do not satisfy the stability requirement of a power distribution system that has a high DG penetration level, which imposes the necessity of developing a new control structure able to overcome the limitations imposed by the fixed structure conventional controllers and limit the penetration of DGs in the overall transient stability of the distribution system. Third, a novel multi-agent based control architecture is proposed for transient stability enhancement for distribution systems with microturbines. The proposed control architecture is hierarchical with one supervisory global control agent and a distributed number of local control agents in the lower layer. Specifically, a central control center supervises and optimizes the overall process, while each microturbine is equipped with its own local control agent.;The control of naval shipboard electric power system is another application of distributed control with multi-agent based structure. In this proposal, the focus is to introduce the concept of multi-agent based control architecture to improve the stability of the shipboard power system during faulty conditions. The effectiveness of the proposed methods is illustrated using a 37-bus IEEE benchmark system and an all-electric naval ship