Swarm Intelligence Computation Research Articles

Design and Application of a Resource Allocation Method for CAEVs Internet of Things Based on Swarm Intelligence Computing

The Internet of Things (IoT) faces significant challenges in the requirements of sensitive task latency, reasonable resource allocation and reliability for resource transactions. This paper introduces a novel method for road resource allocation in the IoT context of connected and autonomous electric vehicles (CAEVs). The proposed algorithm leverages the ant colony algorithm (ACA) to effectively allocate and coordinate road resources within groups of CAEVs. By considering the energy consumption and pheromone volatilization, the allocation and coordination process of road resources are optimized. To improve the linear packet loss of RED, we adopt the advanced ACA and CRED in the NS2 platform. The experimental results demonstrate that the proposed method outperforms the RED algorithm in packet loss rate and delay time, significantly enhancing system efficiency and performance. Furthermore, the combination of the CRED algorithm and ant colony algorithm successfully mitigates short-term congestion and identifies optimized paths with minimal delay.

A Hybrid PSO-DE Intelligent Algorithm for Solving Constrained Optimization Problems Based on Feasibility Rules

In this paper, we study swarm intelligence computation for constrained optimization problems and propose a new hybrid PSO-DE algorithm based on feasibility rules. Establishing individual feasibility rules as a way to determine whether the position of an individual satisfies the constraint or violates the degree of the constraint, which will determine the choice of the individual optimal position and the global optimal position in the particle population. First, particle swarm optimization (PSO) is used to act on the top 50% of individuals with higher degree of constraint violation to update their velocity and position. Second, Differential Evolution (DE) is applied to act on the individual optimal position of each individual to form a new population. The current individual optimal position and the global optimal position are updated using the feasibility rules, thus forming a hybrid PSO-DE intelligent algorithm. Analyzing the convergence and complexity of PSO-DE. Finally, the performance of the PSO-DE algorithm is tested with 12 benchmark functions of constrained optimization and 57 engineering optimization problems, the numerical results show that the proposed algorithm has good accuracy, effectiveness and robustness.

SEIR Modeling of the Italian Epidemic of SARS-CoV-2 Using Computational Swarm Intelligence.

We applied a generalized SEIR epidemiological model to the recent SARS-CoV-2 outbreak in the world, with a focus on Italy and its Lombardy, Piedmont, and Veneto regions. We focused on the application of a stochastic approach in fitting the model parameters using a Particle Swarm Optimization (PSO) solver, to improve the reliability of predictions in the medium term (30 days). We analyzed the official data and the predicted evolution of the epidemic in the Italian regions, and we compared the results with the data and predictions of Spain and South Korea. We linked the model equations to the changes in people’s mobility, with reference to Google’s COVID-19 Community Mobility Reports. We discussed the effectiveness of policies taken by different regions and countries and how they have an impact on past and future infection scenarios.

Data gathering via mobile sink in WSNs using game theory and enhanced ant colony optimization

Optimal performance and improved lifetime are the most desirable design benchmarks for WSNs and the mechanism for data gathering is a major constituent influencing these standards. Several researchers have provided significant evidence on the advantage of mobile sink (MS) in performing effective gathering of relevant data. However, determining the trajectory for MS is an NP-hard-problem. Especially in delay-inevitable applications, it is challenging to select the best-stops or rendezvous points (RPs) for MS and also to design an efficient route for MS to gather data. To provide a suitable solution to these challenges, we propose in this paper, a game theory and enhanced ant colony based MS route selection and data gathering (GTAC-DG) technique. This is a distributed method of data gathering using MS, combining the optimal decision making skill of game theory in selecting the best RPs and computational swarm intelligence of enhanced ant colony optimization in choosing the best path for MS. GTAC-DG helps to reduce data transfer and management, energy consumption and delay in data delivery. The MS moves in a reliable and intelligent trajectory, extending the lifetime and conserving the energy of WSN. The simulation results prove the effectiveness of GTAC-DG in terms of metrics such as energy and network lifetime.

Particle swarm optimization of 2D magnetotelluric data

We implement the particle swarm optimization (PSO) algorithm for the two-dimensional (2D) magnetotelluric (MT) inverse problem. We first validate PSO on two synthetic models of different complexity and then apply it to an MT benchmark for real-field data, the COPROD2 data set (Canada). We pay particular attention to the selection of the PSO input parameters to properly address the complexity of the 2D MT inverse problem. We enhance the stability and convergence of the solution of the geophysical problem by applying the hierarchical PSO with time-varying acceleration coefficients (HPSO-TVAC). Moreover, we parallelize the code to reduce the computation time because PSO is a computationally demanding global search algorithm. The inverse problem was solved for the synthetic data both by giving a priori information at the beginning and by using a random initialization. The a priori information was given to a small number of particles as the initial position within the search space of solutions, so that the swarming behavior was only slightly influenced. We have demonstrated that there is no need for the a priori initialization to obtain robust 2D models because the results are largely comparable with the results from randomly initialized PSO. The optimization of the COPROD2 data set provides a resistivity model of the earth in line with results from previous interpretations. Our results suggest that the 2D MT inverse problem can be successfully addressed by means of computational swarm intelligence.

Performance Analysis of TBC-ACO Routing Protocol with Existing Routing Protocols of Wireless Sensor Networks

Wireless Sensor Networks, have witnessed significant amount of improvement in research across various areas like Routing, Security, Localization, Deployment and above all Energy Efficiency. Congestion is a problem of importance in resource constrained Wireless Sensor Networks, especially for large networks, where the traffic loads exceed the available capacity of the resources . Sensor nodes are prone to failure and the misbehaviour of these faulty nodes creates further congestion. The resulting effect is a degradation in network performance, additional computation and increased energy consumption, which in turn decreases network lifetime. Hence, the data packet routing algorithm should consider congestion as one of the parameters, in addition to the role of the faulty nodes and not merely energy efficient protocols .Nowadays, the main central point of attraction is the concept of Swarm Intelligence based techniques integration in WSN. Swarm Intelligence based Computational Swarm Intelligence Techniques have improvised WSN in terms of efficiency, Performance, robustness and scalability. The main objective of this research paper is to propose congestion aware , energy efficient, routing approach that utilizes Ant Colony Optimization, in which faulty nodes are isolated by means of the concept of trust further we compare the performance of various existing routing protocols like AODV, DSDV and DSR routing protocols, ACO Based Routing Protocol with Trust Based Congestion aware ACO Based Routing in terms of End to End Delay, Packet Delivery Rate, Routing Overhead, Throughput and Energy Efficiency. Simulation based results and data analysis shows that overall TBC-ACO is 150% more efficient in terms of overall performance as compared to other existing routing protocols for Wireless Sensor Networks.

Swarm Superintelligence and Actor Systems

There are different types and levels of intelligence in general and swarm intelligence, in particular. In this paper, we explore the transition from swarm intelligence to swarm super intelligence, i.e., to higher levels in the hierarchy of intelligence. While the conventional approach to swarm intelligence presupposes that it emerges in synergetic organization of simple systems such as ants or fish, to achieve super intelligence, it is necessary to unearth synergy in systems of intelligent agents or actors. To study such systems, we further develop multi-agent approach by creating a new mathematically based type of system models, which is called the System Actor Model allowing concurrency of processes and diversity of actions. Properties of this model are studied and applied to investigation of computational swarm intelligence and super intelligence.

Using simplified swarm optimization on path planning for intelligent mobile robot

This study examined how path planning of intelligent mobile robot could be enhanced by utilizing simplified swarm optimization (SSO) in working environment with irregular obstacles. The conceptual framework of this study was driven from an inspiration of communal behavior of birds flocking and fish schooling. This conceptual framework was supported by swarm intelligence, which is one of the famous research areas in the field of computational swarm intelligence such as particle swarm optimization (PSO) algorithm. Significant observations have been made that mobile robots are significantly affected by path planning problems, and solutions are established how to tackle these problems and numerous weaknesses. Therefore, this study proposes an effective solution which can yield a high quality and efficient mobile robots. The SSO technique was adapted in order to provide an effective solution to the discussed weaknesses. The designed simulation algorithm results showed that SSO does not have assemble in the closed work interface where no path between the initial and the destination points. Obtained results show that when the particle’s path gets into an obstacle area, it is automatically repositioned to an obstacle free area. Autonomy and energy efficiency within the particles are also discussed.

Comparison of particle swarm and bacterial foraging optimization algorithms for therapy planning in HIV/AIDS patients

In HIV/AIDS patients, antiretroviral therapy (ART) is used for reducing the viral load and helps in increasing the life span of the individual. However, severe side effects are associated with the use of antiretroviral drugs. Hence, a treatment schedule, using minimal amount of drugs, is required for maintaining a low viral load and a healthy immune system. The objective of this work is to compute the optimal dosage of antiretroviral drugs for therapy planning in HIV/AIDS patients, using intelligent optimization techniques. In this work, two computational swarm intelligence techniques known as the particle swarm optimization (PSO) and bacterial foraging optimization (BFO) in conjunction with the three-dimensional mathematical model of HIV/AIDS have been used for estimating the optimal drug dosage for administering therapy by minimization of viral load as well as the total drug concentration. Results demonstrate that, using the proposed method, it is possible to achieve minimal viral load and an improved immune system, with the estimated drug dosage. Further, it was observed that the efficiency of BFO (CD4 cells [Formula: see text][Formula: see text]cells/mm3 at seventh year of infection) for estimation of optimal drug dosage is higher than the PSO method (CD4 cells [Formula: see text][Formula: see text]cells/mm3 at seventh year of infection). This work seems to be of high clinical relevance since, at present, ART is the widely used procedure for treatment of HIV infected patients.

An effort to select a preferable metaheuristic model for knowledge discovery in data mining

Swarm intelligence computation is becoming an important topic in the field of decision making, and is successfully applied in many fields, which is indicating a fairly great potential for development. Recently, much research work has been carried out by swarm intelligence in different fields viz. sensor network, robotic management, microwave filter design, data mining applications etc. In this paper, an effort has been made to make a comparison on the performance of fuzzy logic with different membership function, artificial neural network and swarm intelligence models. The models with minimum errors have been given preference for selection towards the decision making of information. The same methods can also be cross-checked by residual analysis and a new unknown data set to verify the earlier proposed observation. Initially the statistical techniques have been applied on the data set to select the preferable models but it has been observed that statistical technique errors are large. Therefore, under the fuzzy logic, different membership functions have been used to select the preferable membership function for fuzzy logic for the available data. In this paper further effort has also been made to change the expression of the computing fitness function of the path in artificial bee colony to achieve the best result.

Computational Swarming: A Cultural Technique for Generative Architecture

After a first wave of digital architecture in the 1990s, the last decade saw some approaches where agent-based modelling and simulation (ABM) was used for generative strategies in architectural design. By taking advantage of the self-organisational capabilities of computational agent collectives whose global behaviour emerges from the local interaction of a large number of relatively simple individuals (as it does, for instance, in animal swarms), architects are able to understand buildings and urbanscapes in a novel way as complex spaces that are constituted by the movement of multiple material and informational elements. As a major, zoo-technological branch of ABM, Computational Swarm Intelligence (SI) coalesces all kinds of architectural elements – materials, people, environmental forces, traffic dynamics, etc. – into a collective population. Thereby, SI and ABM initiate a shift from geometric or parametric planning to time-based and less prescriptive software tools. Agent-based applications of this sort are used to model solution strategies in a number of areas where opaque and complex problems present themselves – from epidemiology to logistics, and from market simulations to crowd control. This article seeks to conceptualise SI and ABM as a fundamental and novel cultural technique for governing dynamic processes, taking their employment in generative architectural design as a concrete example. In order to avoid a rather conventional application of philosophical theories to this field, the paper explores how the procedures of such technologies can be understood in relation to the media-historical concept of Cultural Techniques.

Computational Swarming: A Cultural Technique for Generative Architecture

After a first wave of digital architecture in the 1990s, the last decade saw some approaches where agent-based modelling and simulation (ABM) was used for generative strategies in architectural design. By taking advantage of the self-organisational capabilities of computational agent collectives whose global behaviour emerges from the local interaction of a large number of relatively simple individuals (as it does, for instance, in animal swarms), architects are able to understand buildings and urbanscapes in a novel way as complex spaces that are constituted by the movement of multiple material and informational elements. As a major, zoo-technological branch of ABM, Computational Swarm Intelligence (SI) coalesces all kinds of architectural elements – materials, people, environmental forces, traffic dynamics, etc. – into a collective population. Thereby, SI and ABM initiate a shift from geometric or parametric planning to time-based and less prescriptive software tools. Agent-based applications of this sort are used to model solution strategies in a number of areas where opaque and complex problems present themselves – from epidemiology to logistics, and from market simulations to crowd control. This article seeks to conceptualise SI and ABM as a fundamental and novel cultural technique for governing dynamic processes, taking their employment in generative architectural design as a concrete example. In order to avoid a rather conventional application of philosophical theories to this field, the paper explores how the procedures of such technologies can be understood in relation to the media-historical concept of Cultural Techniques.

The Study on Electric Power System Based on Swarm Intelligence

In this paper, we introduce the swarm intelligence computation and its applications in power system. Because swarm intelligence does not need any precondition of centralized control and global model, it is very suitable to solve large scale power system nonlinear optimization problems which are hard to establish effective formalized models and difficult to be solved by traditional methods. In order to apply swarm intelligence better in power system, we propose two central research directions in the future: (1) The mathematical basis of swarm intelligence is unsubstantial and it lacks profound and pervasive theoretical analysis, so we must analysis its convergence and selection of parameters, especially the parameter selection of large scale power system optimization problems. (2) Because swarm intelligence is internally parallel, we should realize it based on the parallel computation theory. This work will also be helpful for the real-time need of power system.