Dynamic Agricultural Pest Classification Using Enhanced SAO-CNN and Swarm Intelligence Optimization for UAVs

Nowadays, the unmanned aerial vehicle (UAV) is widely used in various military and civil scenarios due to its advantages of flexibility, low cost and expansibility. As a critical technology to improve the degree of UAV autonomy, path planning has become a hot issue in recent years. UAV path planning is an optimization problem with a series of constraints essentially, whose optimal or sub-optimal solution can be obtained by swarm intelligence (SI) optimization with the superiorities of expansibility, parallel processing and compatibility for the UAV group. Therefore, this paper summarizes UAV path planning approaches based on SI optimization in recent years, and analyzes application dimension, ability of avoiding local optimum, expansibility for UAV group and real-time performance of different approaches. Finally, we suggest the applicable scenarios of various algorithms.KeywordsUnmanned aerial vehiclePath planningSwarm intelligence optimization

A survey of multiple types of text summarization with their satellite contents based on swarm intelligence optimization algorithms

Unmanned aerial vehicles (UAVs) swarm technology has been a hot topic. The concept of swarm comes from nature, such as the cooperation of bee colony or ant colony, so the research of swarm intelligence (SI) optimization algorithm inspired by swarm activities in nature is beneficial to the development of swarm UAVs technology. The characteristics and principles of eleven SI algorithms are described and analyzed in the paper. The article also analyzed how to combine SI and multi-UAV task assignments. Furthermore, the prospects for development of the SI are discussed.

A large amount of calculation exists in a complex engineering optimization problem. The swarm intelligence algorithm can improve calculation efficiency and accuracy of complex engineering optimization. In the existing research, the surrogate model and the swarm intelligence algorithm are only two independent tools to solve the optimization problem. In this paper, we propose the surrogate-assisted crow swarm intelligent search optimization algorithm (SACSA) by combining the characteristics of swarm intelligence algorithm and surrogate model. The proposed algorithm utilizes the initial samples to construct the surrogate model, and then the improved crow search algorithm (CSA) is applied to obtain optimal solution. Finally, the proposed algorithm is compared with EGO, MSSR, ARSM-ISES, AMGO and SEUMRE, MPS, HAM algorithms. The comparison results show that the proposed algorithm can find a global optimal solution with fewer samples and is beneficial to improving the efficiency and accuracy of calculation.

Robot swarms provide a robust approach for performing common tasks in which the emergence of collective capabilities outperforms the addition of the individual ones. They are frequently used in surveillance or defence systems where resilience is a must. In this article we propose a swarm of drones capable of surrounding and escorting different types of targets such as a rogue drone or a ground vehicle. We use swarm intelligence and evolutionary optimisation to support the swarm self-organisation using a set of optimal parameters. Our experiments were focused on analysing the properties of the swarm of drones as well as assessing its scalability and fault tolerance. We have used computer simulations to test a variety of different initial drone positions and target trajectories. Additionally, we have validated our proposal through experiments using real-world drones. The achieved results show that our formation system has successfully built stable formations when it was properly configured. It has worked with swarms of five, ten, and twenty drones, and has also been able to recover in the majority of cases in which some drones have failed.

In this paper, an off-line optimized PID controller algorithm, utilizing swarm intelligence is developed, for a laboratory-scaled water distribution system. The swarm intelligence optimization techniques are simulated in MATLAB and Simulink Tools. The two techniques used are Particle Swarm Intelligence (PSO) and Grey Wolf Optimization (GWO). An efficient controller produces a better system, i.e. the attainment of optimized level of the reservoir tank of the system in meeting customers demand. Hence by implementing the optimization techniques, the performance of the PID-controller, is improved, by generating the optimal values of the PID parameters, thus improving the system’s performance. When the optimized parameters are applied to the system, output response of the system using PSO and GWO are compared and analyzed. From the results obtained, it can be observed that GWO produces better results than PSO, specifically in the reduction of the system performance’s overshoot.KeywordsParticle Swarm OptimizationGrey Wolf OptimizationSwarm intelligencePID controllerWater distribution system

Swarm intelligence (SI) optimization algorithms are fast and robust global optimization methods, and have attracted significant attention due to their ability to solve complex optimization problems. The underlying idea behind all SI algorithms is similar, and various SI algorithms differ only in their details. In this paper we discuss the algorithmic equivalence of particle swarm optimization (PSO) and various other newer SI algorithms, including the shuffled frog leaping algorithm (SFLA), the group search optimizer (GSO), the firefly algorithm (FA), artificial bee colony algorithm (ABC) and the gravitational search algorithm (GSA). We find that the original versions of SFLA, GSO, FA, ABC, and GSA, are all algorithmically identical to PSO under certain conditions. We discuss their diverse biological motivations and algorithmic details as typically implemented, and show how their differences enhance the diversity of SI research and application. Then we numerically compare SFLA, GSO, FA, ABC, and GSA, with basic and advanced versions on some continuous benchmark functions and combinatorial knapsack problems. Empirical results show that an advanced version of ABC performs best on the continuous benchmark functions, and advanced versions of SFLA and GSA perform best on the combinatorial knapsack problems. We conclude that although these SI algorithms are conceptually equivalent, their implementation details result in notably different performance levels.

Due to the efficiency and efficacy in performance to tackle complex optimization problems, swarm intelligence (SI) optimizers, newly emerged as nature-inspired algorithms, have gained great interest from researchers over different fields. A large number of SI optimizers and their extensions have been developed, which drives the need to comprehensively review the characteristics of each algorithm. Hence, a generalized framework laid upon the fundamental principles from which SI optimizers are developed is crucial. This research takes a multidisciplinary view by exploring research motivations from biology, psychology, computing and engineering. A learning–interaction–diversification (LID) framework is proposed where learning is to understand the individual behavior, interaction is to describe the swarm behavior, and diversification is to control the population performance. With the LID framework, 22 state-of-the-art SI algorithms are characterized, and nine representative ones are selected to review in detail. To investigate the relationships between LID properties and algorithmic performance, LID-driven experiments using benchmark functions and real-world problems are conducted. Comparisons and discussions on learning behaviors, interaction relations and diversity control are given. Insights of the LID framework and challenges are also discussed for future research directions.

Swarm intelligence optimization algorithm is a heuristic search algorithm based on the swarm intelligent behavior of biology, which shows excellent performance in deal with complex optimization problems. On the basis of analyzing particle swarm optimization algorithm, ant colony algorithm and artificial bee colony algorithm, the paper presents a unified frame of swarm intelligence optimization algorithm that is helpful for improving and perfecting swarm intelligence optimization algorithm.Keywordsswarm intelligence optimization algorithmparticle swarm optimization algorithmant colony algorithmartificial bee colony algorithmunified frame

The use of nature-inspired meta-heuristics to tackle complex optimization problems is steadily gaining popularity within a rapidly evolving world. Swarm intelligence (SI) optimization motivated by behavior of community-based organisms of flocks of birds, schools of fish, colonies of ants and bees performs the search through agents whose trajectories are primarily adjusted stochastically and sporadically deterministically, by golden rules drawn from Mother Nature. Each entity within the swarm is swayed by its individual ‘best’ and group’s ‘best’ position while moving randomly to converge to optimal through competition and cooperation. The sparrow search algorithm (SSA), developed by Xuea and Shen (Xue and Shen in Syst Sci Cont Eng 8:22–34, 2020) is a very recent SI approach that adopts the sparrow producer–scrounger model metaphorically for designing optimum searching strategies, inspired by the foraging, anti-predation behavior, and the overall group wisdom of sparrows. SSA has been experimented on some hard benchmark test functions to test its effectiveness and thereafter, applied in slope-stability problems in searching the critical failure surface. The objective function to be optimized is the factor of safety against failure given by Bishop's (Bishop in Geotechnique 5:7–17, 1955) simplified method. Results show SSA is a strong contender to bio-inspired methods like genetic algorithms, big-bang big-crunch, simulated annealing, and artificial bee colony algorithms. The study illustrates the flexibility, efficiency, and robustness of the methodology in function optimization.

Gaussian Barebone Salp Swarm Algorithm with Stochastic Fractal Search for medical image segmentation: A COVID-19 case study

The paper provides insight into various swarm intelligence based routing protocols for Internet of Things (IoT), which are currently available for the Mobile Ad-hoc networks (MANETs) and wireless sensor networks (WSNs). There are several issues which are limiting the growth of Internet of Things. These include the reliability, link failures, routing, heterogeneity etc. The MANETs and WSNs routing issues impose almost same requirements for IoT routing mechanism. The recent work of the worldwide researchers is focused on this area. protocols are based on the principles of swarm intelligence. The swarm intelligence is applied to achieve the optimality and the efficiency in solving the complex, multi-hop and dynamic requirements of the wireless networks. The application of the ACO technique tries to provide answers to many routing issues. Using the swarm intelligence and ant colony optimization principles, it has been seen that, the protocols’ efficiency definitely increases and also provides more scope for the development of more robust, reliable and efficient routing protocols for the IoT. As the various standard protocols available for MANETs and WSNs are not reliable enough, the paper finds the need of some efficient routing algorithms for IoT.

Swarm-based intelligence is a recently developed area of computational intelligence that offers a powerful framework for solving complex optimization problems. It has found applications in various scientific fields where optimization of one or more functions is required. There are several methods developed under the umbrella of swarm intelligence, and particle swarm optimization (PSO) is one of them. This chapter presents swarm intelligence and its applications in industrial engineering as well as nuclear power plants and PSO is used to illustrate the potential of such an implementation. The roadmap of the chapter is as follows: Sec. 9.1 provides an introduction to swarm intelligence and the following section presents particle swarm optimization. A discussion on the implementation of PSO in industrial engineering problems is given in Sec. 9.3 while PSO applications in nuclear power plants (NPP) are presented in Sec. 9.4. Section 9.5 concludes the chapter.

Chapter 2 - Nature-Inspired Algorithms and Systems

PurposeThe purpose of this paper is to provide insight into various swarm intelligence-based routing protocols for Internet of Things (IoT), which are currently available for the Mobile Ad-hoc networks (MANETs) and wireless sensor networks (WSNs). There are several issues which are limiting the growth of IoT. These include privacy, security, reliability, link failures, routing, heterogeneity, etc. The routing issues of MANETs and WSNs impose almost the same requirements for IoT routing mechanism. The recent work of worldwide researchers is focused on this area.Design/methodology/approachThe paper provides the literature review for various standard routing protocols. The different comparative analysis of the routing protocols is done. The paper surveys various routing protocols available for the seamless connectivity of things in IoT. Various features, advantages and challenges of the said protocols are discussed. The protocols are based on the principles of swarm intelligence. Swarm intelligence is applied to achieve optimality and efficiency in solving the complex, multi-hop and dynamic requirements of the wireless networks. The application of the ant colony optimization technique tries to provide answers to many routing issues.FindingsUsing the swarm intelligence and ant colony optimization principles, it has been seen that the protocols’ efficiency definitely increases and also provides more scope for the development of more robust, reliable and efficient routing protocols for the IoT.Research limitations/implicationsThe existing protocols do not solve all reliability issues and efficient routing is still not achieved completely. As of now no techniques or protocols are efficient enough to cover all the issues and provide the solution. There is a need to develop new protocols for the communication which will cater to all these needs. Efficient and scalable routing protocols adaptable to different scenarios and network size variation capable to find optimal routes are required.Practical implicationsThe various routing protocols are discussed and there is also an introduction to new parameters which can strengthen the protocols. This can lead to encouragement of readers, as well as researchers, to analyze and develop new routing algorithms.Social implicationsThe paper provides better understanding of the various routing protocols and provides better comparative analysis for the use of swarm-based research methodology in the development of routing algorithms exclusively for the IoT.Originality/valueThis is a review paper which discusses the various routing protocols available for MANETs and WSNs and provides the groundwork for the development of new intelligent routing protocols for IoT.