A survey of swarm intelligence for dynamic optimization: algorithms and applications

Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given

Differential Evolution in Wireless Communications: A Review

Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless communication. The detailed history, characteristics, strengths, variants and weaknesses of DE were presented. Seven broad areas were identified as different domains of application of DE in wireless communications. It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied. Others areas are quality of service, updating mechanism where candidate positions learn from a large diversified search region, security and related field applications. Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. Different research areas can be explored and DE will continue to be utilized in this contex

Swarm intelligence and its applications to wireless ad hoc and sensor networks.

Swarm intelligence, as inspired by natural biological swarms, has numerous powerful properties for distributed problem solving in complex real world applications such as optimisation and control. Swarm intelligence properties can be found in natural systems such as ants, bees and birds, whereby the collective behaviour of unsophisticated agents interact locally with their environment to explore collective problem solving without centralised control. Recent advances in wireless communication and digital electronics have instigated important changes in distributed computing. Pervasive computing environments have emerged, such as large scale communication networks and wireless ad hoc and sensor networks that are extremely dynamic and unreliable. The network management and control must be based on distributed principles where centralised approaches may not be suitable for exploiting the enormous potential of these environments. In this thesis, we focus on applying swarm intelligence to the wireless ad hoc and sensor networks optimisation and control problems. Firstly, an analysis of the recently proposed particle swarm optimisation, which is based on the swarm intelligence techniques, is presented. Previous stability analysis of the particle swarm optimisation was restricted to the assumption that all of the parameters are non random since the theoretical analysis with the random parameters is difficult. We analyse the stability of the particle dynamics without these restrictive assumptions using Lyapunov stability and passive systems concepts. The particle swarm optimisation is then used to solve the sink node placement problem in sensor networks. Secondly, swarm intelligence based routing methods for mobile ad hoc networks are investigated. Two protocols have been proposed based on the foraging behaviour of biological ants and implemented in the NS2 network simulator. The first protocol allows each node in the network to choose the next node for packets to be forwarded on the basis of mobility influenced routing table. Since mobility is one of the most important factors for route changes in mobile ad hoc networks, the mobility of the neighbour node using HELLO packets is predicted and then translated into a pheromone decay as found in natural biological systems. The second protocol uses the same mechanism as the first, but instead of mobility the neighbour node remaining energy level and its drain rate are used. The thesis clearly shows that swarm intelligence methods have a very useful role to play in the management and control iv problems associated with wireless ad hoc and sensor networks. This thesis has given a number of example applications and has demonstrated its usefulness in improving performance over other existing methods

Energy Efficient Routing Algorithms for Wireless Sensor Networks and Performance Evaluation of Quality of Service for IEEE 802.15.4 Networks

The popularity of Wireless Sensor Networks (WSN) have increased tremendously in recent time due to growth in Micro-Electro-Mechanical Systems (MEMS) technology. WSN has the potentiality to connect the physical world with the virtual world by forming a network of sensor nodes. Here, sensor nodes are usually battery-operated devices, and hence energy saving of sensor nodes is a major design issue. To prolong the network‘s lifetime, minimization of energy consumption should be implemented at all layers of the network protocol stack starting from the physical to the application layer including cross-layer optimization. In this thesis, clustering based routing protocols for WSNs have been discussed. In cluster-based routing, special nodes called cluster heads form a wireless backbone to the sink. Each cluster heads collects data from the sensors belonging to its cluster and forwards it to the sink. In heterogeneous networks, cluster heads have powerful energy devices in contrast to homogeneous networks where all nodes have uniform and limited resource energy. So, it is essential to avoid quick depletion of cluster heads. Hence, the cluster head role rotates, i.e., each node works as a cluster head for a limited period of time. Energy saving in these approaches can be obtained by cluster formation, cluster-head election, data aggregation at the cluster-head nodes to reduce data redundancy and thus save energy. The first part of this thesis discusses methods for clustering to improve energy efficiency of homogeneous WSN. It also proposes Bacterial Foraging Optimization (BFO) as an algorithm for cluster head selection for WSN. The simulation results show improved performance of BFO based optimization in terms of total energy dissipation and no of alive nodes of the network system over LEACH, K-Means and direct methods. IEEE 802.15.4 is the emerging next generation standard designed for low-rate wireless personal area networks (LR-WPAN). The second part of the work reported here in provides performance evaluation of quality of service parameters for WSN based on IEEE 802.15.4 star and mesh topology. The performance studies have been evaluated for varying traffic loads using MANET routing protocol in QualNet 4.5. The data packet delivery ratio, average end-to-end delay, total energy consumption, network lifetime and percentage of time in sleep mode have been used as performance metrics. Simulation results show that DSR (Dynamic Source Routing) performs better than DYMO (Dynamic MANET On-demand) and AODV (Ad–hoc On demand Distance Vector) routing protocol for varying traffic loads rates

EIABC Optimization Approach for DSPRP in MANET

The very many optimization techniques like GA, PSO and ABC aid in crystallizing and addressing the static shortest path in the realm of wireless network routing. The motion of MANET is dynamic and hence the shortest path routing problem in MANET manifests into a dynamic optimization problem. The nodes are instilled with an awareness of the environmental conditions by making them operational through intelligence routing becomes a key concern as it has a significant impact towards network performance. The paper attempts to exploit and utilize Artificial Bee Colony to solve MANET because shortest Path problem turns out to be a dynamic optimization problem in MANETs. MANETs are kept and considered target systems because they do represent the next generation wireless network. The results of experiment explicate that Artificial Bee Colony is steadfast to adapt to the gradations in the environment

Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks

This book presents collective works published in the recent Special Issue (SI) entitled "Recent Developments on Mobile Ad-Hoc Networks and Vehicular Ad-Hoc Networks”. These works expose the readership to the latest solutions and techniques for MANETs and VANETs. They cover interesting topics such as power-aware optimization solutions for MANETs, data dissemination in VANETs, adaptive multi-hop broadcast schemes for VANETs, multi-metric routing protocols for VANETs, and incentive mechanisms to encourage the distribution of information in VANETs. The book demonstrates pioneering work in these fields, investigates novel solutions and methods, and discusses future trends in these field

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

Optimising routing and trustworthiness of ad hoc networks using swarm intelligence

This thesis was submitted for the degree of Doctor of Philsophy and awarded by Brunel UniversityThis thesis proposes different approaches to address routing and security of MANETs using swarm technology. The mobility and infrastructure-less of MANET as well as nodes misbehavior compose great challenges to routing and security protocols of such a network. The first approach addresses the problem of channel assignment in multichannel ad hoc networks with limited number of interfaces, where stable route are more preferred to be selected. The channel selection is based on link quality between the nodes. Geographical information is used with mapping algorithm in order to estimate and predict the links’ quality and routes life time, which is combined with Ant Colony Optimization (ACO) algorithm to find most stable route with high data rate. As a result, a better utilization of the channels is performed where the throughput increased up to 74% over ASAR protocol. A new smart data packet routing protocol is developed based on the River Formation Dynamics (RFD) algorithm. The RFD algorithm is a subset of swarm intelligence which mimics how rivers are created in nature. The protocol is a distributed swarm learning approach where data packets are smart enough to guide themselves through best available route in the network. The learning information is distributed throughout the nodes of the network. This information can be used and updated by successive data packets in order to maintain and find better routes. Data packets act like swarm agents (drops) where they carry their path information and update routing information without the need for backward agents. These data packets modify the routing information based on different network metrics. As a result, data packet can guide themselves through better routes. In the second approach, a hybrid ACO and RFD smart data packet routing protocol is developed where the protocol tries to find shortest path that is less congested to the destination. Simulation results show throughput improvement by 30% over AODV protocol and 13% over AntHocNet. Both delay and jitter have been improved more than 96% over AODV protocol. In order to overcome the problem of source routing introduced due to the use of the ACO algorithm, a solely RFD based distance vector protocol has been developed as a third approach. Moreover, the protocol separates reactive learned information from proactive learned information to add more reliability to data routing. To minimize the power consumption introduced due to the hybrid nature of the RFD routing protocol, a forth approach has been developed. This protocol tackles the problem of power consumption and adds packets delivery power minimization to the protocol based on RFD algorithm. Finally, a security model based on reputation and trust is added to the smart data packet protocol in order to detect misbehaving nodes. A trust system has been built based on the privilege offered by the RFD algorithm, where drops are always moving from higher altitude to lower one. Moreover, the distributed and undefined nature of the ad hoc network forces the nodes to obligate to cooperative behaviour in order not to be exposed. This system can easily and quickly detect misbehaving nodes according to altitude difference between active intermediate nodes

Review and Classification of Bio-inspired Algorithms and Their Applications

Scientists have long looked to nature and biology in order to understand and model solutions for complex real-world problems. The study of bionics bridges the functions, biological structures and functions and organizational principles found in nature with our modern technologies, numerous mathematical and metaheuristic algorithms have been developed along with the knowledge transferring process from the lifeforms to the human technologies. Output of bionics study includes not only physical products, but also various optimization computation methods that can be applied in different areas. Related algorithms can broadly be divided into four groups: evolutionary based bio-inspired algorithms, swarm intelligence-based bio-inspired algorithms, ecology-based bio-inspired algorithms and multi-objective bio-inspired algorithms. Bio-inspired algorithms such as neural network, ant colony algorithms, particle swarm optimization and others have been applied in almost every area of science, engineering and business management with a dramatic increase of number of relevant publications. This paper provides a systematic, pragmatic and comprehensive review of the latest developments in evolutionary based bio-inspired algorithms, swarm intelligence based bio-inspired algorithms, ecology based bio-inspired algorithms and multi-objective bio-inspired algorithms

免疫学的および進化的アルゴリズムに基づく改良された群知能最適化に関する研究

富山大学・富理工博甲第175号・楊玉・2020/3/24富山大学202