An enhanced decentralized artificial immune-based strategy formulation algorithm for swarms of autonomous vehicles

Stogiannos, Marios; Alexandridis, Alex; Sarimveis, Haralambos

doi:10.1016/j.asoc.2020.106135

Cited by 11 publications

(5 citation statements)

References 29 publications

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“…The immune optimization algorithm inspired by the immune information processing mechanism has been successfully applied in various engineering fields in recent years because of its strong optimization ability [26][27][28]. In order to improve the path planning accuracy and efficiency of the ship welding robot, the immune optimization algorithm is introduced in this paper.…”

Section: Immune Algorithm Based On Cluster Analysis and Self-circulationmentioning

confidence: 99%

Multi-Objective Immune Optimization of Path Planning for Ship Welding Robot

et al. 2023

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In order to improve the welding efficiency of the ship welding robot, the path planning of the welding robot based on immune optimization is proposed by taking the welding path length and energy loss as the optimization goals. First, on the basis of the definition of the path planning of the welding robot, the grid modeling of the robot’s working environment and the triangular modeling of the welding weldments are carried out. Then, according to the working process of the welding robot, the length objective function, including the welded seam path and the welding torch path without welding, is constructed, and the energy loss function is constructed based on the kinematics and Lagrange function. Finally, the immune optimization algorithm based on cluster analysis and self-circulation is introduced to realize the multi-objective optimization of the path planning for the ship welding robot. The test results of four kinds of ship welding weldments show that compared with the simple genetic algorithm, immune genetic algorithm, ant colony algorithm, artificial bee colony, particle swarm optimization, and immune cloning optimization, the proposed multi-objective immune planning algorithm is the best in terms of planning path length, energy consumption, and stability. Furthermore, the average shortest path and its standard deviation, the average minimum energy consumption and its standard deviation, and the average lowest convergence generation and its standard deviation are reduced by an average of 9.03%, 54.04%, 8.23%, 19.10%, 27.84%, and 52.25%, respectively, which fully verifies the effectiveness and superiority of the proposed welding robot path planning algorithm.

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Section: Immune Algorithm Based On Cluster Analysis and Self-circulationmentioning

confidence: 99%

Multi-Objective Immune Optimization of Path Planning for Ship Welding Robot

et al. 2023

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“…This metric is similar to the one used by Esterle and Lewis (2017), Esterle and Lewis (2020) and Frasheri et al (2020) who used a k-coverage metric that calculated the amount of time a target spent being tracked by at least k agents. Stogiannos et al (2020) measured the level of exploration and exploitation by tracking three metrics: 1) the minimum distance between agent pairs, 2) the sum of the distances between detected targets and their closest agents, and 3) the distance moved by each agent over the course of one time-step. While often not explicitly stated, the distance between a robot and its neighbors is the most common measure of diversity as most control methods tend to focus on preventing excessive aggregation or spatial distribution (Hereford and Siebold, 2010;Meyer-Nieberg et al, 2013;Lv et al, 2016;Meyer-Nieberg, 2017;Chen and Huang, 2019;Coquet et al, 2019;Coquet et al, 2021).…”

Section: Spatial Distribution Based Metricsmentioning

confidence: 99%

“…Using a similar strategy Sun et al (2001), also demonstrated the AIS's potential in situations where is a large influx in quantity of tasks to be performed by the system. The same strategy was applied by Razali et al (2010) and Razali et al (2012) in a dynamic shepherding scenario, as well as by Stogiannos et al (2020) in a dynamic target tracking task.…”

Section: Artificial Immune System Strategiesmentioning

confidence: 99%

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

2022

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Multi-agent systems and multi-robot systems have been recognized as unique solutions to complex dynamic tasks distributed in space. Their effectiveness in accomplishing these tasks rests upon the design of cooperative control strategies, which is acknowledged to be challenging and nontrivial. In particular, the effectiveness of these strategies has been shown to be related to the so-called exploration–exploitation dilemma: i.e., the existence of a distinct balance between exploitative actions and exploratory ones while the system is operating. Recent results point to the need for a dynamic exploration–exploitation balance to unlock high levels of flexibility, adaptivity, and swarm intelligence. This important point is especially apparent when dealing with fast-changing environments. Problems involving dynamic environments have been dealt with by different scientific communities using theory, simulations, as well as large-scale experiments. Such results spread across a range of disciplines can hinder one’s ability to understand and manage the intricacies of the exploration–exploitation challenge. In this review, we summarize and categorize the methods used to control the level of exploration and exploitation carried out by an multi-agent systems. Lastly, we discuss the critical need for suitable metrics and benchmark problems to quantitatively assess and compare the levels of exploration and exploitation, as well as the overall performance of a system with a given cooperative control algorithm.

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“…The particular AIS technique caught high attention in literature is CS [24]. Additionally, AISbased approaches originate from the principium of the BIS, which attempts to resolve the complex issue of protecting a creature against natural threats by dispersing information to a swarm encompassing an enormous numeral of proxies [70]. And then, the CS explanation is utilized to grabble the optimum attribute dataset, which may gain a dominant manifestation than both present local and complete attribute extraction algorithms [78].…”

Section: Literature Reviewmentioning

confidence: 99%

A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

Chen

2022

Int J Comput Intell Syst

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This research attempts to reinforce the cultivating expression of radial basis function neural network (RBFnet) through computational intelligence (CI) and swarm intelligence (SI) learning methods. Consequently, the artificial immune system (AIS) and ant colony optimization (ACO) approaches are utilized to cultivate RBFnet for function approximation issue. The proposed hybridization of AIS and ACO approaches optimization (HIAO) algorithm combines the complementarity of exploitation and exploration to realize problem solving. It allows the solution domain having the advantages of intensification and diversification, which further avoids the situation of immature convergence. In addition, the empirical achievements have confirmed that the HIAO algorithm not only obtained the best accurate function approximation for theoretically standard nonlinear problems, it can be further applied on the instance solving for practical crude oil spot price prediction.

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An enhanced decentralized artificial immune-based strategy formulation algorithm for swarms of autonomous vehicles

Cited by 11 publications

References 29 publications

Multi-Objective Immune Optimization of Path Planning for Ship Welding Robot

Multi-Objective Immune Optimization of Path Planning for Ship Welding Robot

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

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