Search Results (29)

Titanium dioxide nanostructures are extensively produced and utilized in various industries. Concerns have been raised about this material’s less researched environmental impact. This study investigates the indirect toxicity of TiO₂ nanoparticles (NPs) on ant communication via the photocatalytic degradation of ant trail pheromones. Foraging experiments with Lasius niger demonstrated that TiO₂-treated pathways were avoided by ants, suggesting trail pheromone degradation. Photocatalytic tests confirmed the degradation of the pheromone component (R)-(-)-mellein under UV-A irradiation in the presence of Evonik Aeroxide P25 TiO₂. The nanosized titania was characterized using X-ray diffraction (XRD), transmission electron microscopy (TEM), and diffuse reflectance spectroscopy (DRS). These findings indicate that TiO₂ NPs can disrupt ant communication, potentially leading to significant ecological consequences. Full article

Online peer support platforms are vital, scalable resources for mental health, yet their effectiveness is frequently undermined by inefficient user matching, severe participation inequality, and subsequent “super-helper” burnout. This study introduces TrailMap, a novel peer-matching algorithm inspired by the decentralised foraging strategies of ant colonies. By treating user interactions as paths that gain or lose “pheromone” based on helpfulness ratings, the system enables the community to collectively and adaptively identify its most effective helpers. A two-phase validation study was conducted. First, an agent-based simulation demonstrated that TrailMap reduced the mean time to a helpful response by over 70% and improved workload equity compared to random routing. Second, a four-week randomised controlled pilot study with human participants confirmed these gains, showing a 76% reduction in median wait time and significantly higher perceived helpfulness ratings. The findings suggest that by balancing the workload, TrailMap enhances not only the efficiency but also the socio-technical sustainability of online support communities. TrailMap provides a practical, nature-inspired method for building more resilient and equitable online support communities, enhancing access to effective mental health support. Full article

Humanoid soccer robots operate in dynamic, unpredictable, and often partially observable settings. Effective teamwork, sound decision-making, and real-time collaboration are critical to competitive performance. In this paper, a biologically inspired swarm-intelligence framework for humanoid soccer is proposed, comprising (1) a low-overhead communication User Datagram Protocol (UDP) optimized for minimal bandwidth and graceful degradation under packet loss; (2) an Ant Colony Optimization (ACO)-based decentralized role allocation mechanism that dynamically assigns attackers, midfielders, and defenders based on real-time pheromone trails and local fitness metrics; (3) a Reynolds’ flocking-based formation control scheme, modulated by role-specific weighting to ensure fluid transitions between offensive and defensive formations; and (4) an adaptive behavior layer integrating lightweight reinforcement signals and proactive failure-recovery strategies to maintain cohesion under robot dropouts. Simulations demonstrate a 25–40% increase in goals scored and an 8–10% boost in average ball possession compared to centralized baselines. Full article

In recent years, an increasing number of accidents at ski resorts have raised significant safety concerns. To address these issues, it is essential to understand skiing traffic and the underlying dynamics. We collected 225 trajectories, which were analyzed after a correction process. To obtain a quantitative classification of typical trajectories we focus on three main quantities: turning points, minimum distance, and the fundamental diagram. Our objective was to analyze these trajectories in depth and identify key statistical properties. Our findings indicate that three factors—turning angle, curvature, and velocity change—can be used to accurately identify turning points and classify skiers’ movement styles. We found that aggressive skiers tend to exhibit larger and less stable turning angles, while conservative skiers demonstrate a more controlled style, characterized by smaller, more stable turns. This is consistent with observations made for the distribution of the minimum distance to other skiers. Furthermore, we have derived a fundamental diagram which is an important characteristic of any traffic system. It is found share more similarities with the fundamental diagram of ant trails than those of highway traffic. Full article

The expansion of the tourism industry has led to the development of various methods to find optimal tourism paths. However, planning tourism paths in hilly areas remains complex and has specific challenges. Different algorithms have been used to plan tourism paths in flat and hilly terrains, including the traditional Ant Colony Optimization (ACO). Although widely used, this algorithm faces a number of limitations due to its slow implementation and pheromone update rules. This paper introduces a new approach to overcome these limitations. It presents a method for efficiently optimizing tourism paths in hilly areas based on an improved version of the ACO algorithm. The limitations of the traditional ACO and the Genetic Algorithm (GA) are addressed by improving pheromone updating techniques and implementing new initialization parameters. This approach provides a comprehensive and efficient method for planning hiking trails in hilly regions, considering dynamic tourism objectives such as temperature, atmospheric pressure, and health status. The proposed method is implemented to develop tourist routes in the hilly Jebel Marra region in Western Sudan. A comparison is provided between the effectiveness of this approach and the GA and traditional ACO algorithms. The advantage of the proposed approach is illustrated by results showing an optimization time of 0 points and 27 s compared to 0 points and 45 s and 0 points and 40 s for GA and ACO, respectively. Full article

The utilization of Harris Hawks Optimization (HHO) for Multi-Objective Optimal Power Flow (MaO-OPF) challenges presented in this paper is both novel and compelling, as this approach has not been previously applied to these types of optimization problems. HHO, which shares characteristics with ant behavior, demonstrates significant strength in addressing high-dimensional, nonlinear optimization issues within power systems. In this study, HHO is implemented on an IEEE 30-bus power system, optimizing six competing objectives: minimizing total fuel cost, emissions, active power loss, reactive power loss, reducing voltage deviation, and enhancing voltage steady state. The effectiveness of HHO is assessed by comparing its performance to two alternative methods, MOEA/D-DRA and NSGA-III. Experimental results reveal that solutions derived from HHO exhibit superior convergence, enhanced diversity maintenance, and higher quality Pareto-optimal solutions compared to the MOEA/D trail algorithms. The research breaks new ground in the application of the Harris Hawks Optimization (HHO) algorithm to the Multi-Objective Optimal Power Flow (MaO-OPF) problem. The restructuring not only incorporates self-adaptive constraint-handling techniques and dynamic exploration exploitation strategies, but also addresses the more pressing requirements of modern power systems with even better convergence, and both sequential and global computational efficiency than existing skill. This approach proves to be a powerful and effective solution for addressing the complex challenges associated with MaO, enabling power systems to manage multiple conflicting objectives more efficiently. Full article

The potential of subdominant ants of the Formica fusca group and their role in forests are still underestimated. Since ant behaviour is dependent on colony size, studying the functional organisation of nest complexes (NC) is most promising for a more accurate assessment of species capabilities. The study focused on the main ecological and ethological issues of the life activity of Formica lemani Bondroit within large NC (>150 nests) and beyond. After preliminary mapping of the F. lemani NC (main nests, trails, foraging trees), off-nest activity, aggressiveness, and trophobiotic relationships with aphids in and outside the NC territory were studied. Within the NC, the dynamic density, the intensity of movement on trails, and aggressiveness of F. lemani were significantly higher than beyond; the range of symbiont aphids was twice as small, with aphids on birches playing a key role in carbohydrate nutrition of F. lemani. The latter ensures accelerated restoration of trophobiotic interactions in spring and stability of the food supply until autumn. Combined with the lack of pressure from F. rufa group ants, this allowed F. lemani to maintain high population densities, and significantly increased its competitiveness, and role in plant protection against phytophages. Full article

Sol g 2 is the major protein in Solenopsis geminata fire ant venom. It shares the highest sequence identity with Sol i 2 (S. invicta) and shares high structural homology with LmaPBP (pheromone-binding protein (PBP) from the cockroach Leucophaea maderae). We examined the specific Sol g 2 protein ligands from fire ant venom. The results revealed that the protein naturally formed complexes with hydrocarbons, including decane, undecane, dodecane, and tridecane, in aqueous venom solutions. Decane showed the highest affinity binding (K_d) with the recombinant Sol g 2.1 protein (rSol g 2.1). Surprisingly, the mixture of alkanes exhibited a higher binding affinity with the rSol g 2.1 protein compared to a single one, which is related to molecular docking simulations, revealing allosteric binding sites in the Sol g 2.1 protein model. In the trail-following bioassay, we observed that a mixture of the protein sol g 2.1 and hydrocarbons elicited S. geminata worker ants to follow trails for a longer time and distance compared to a mixture containing only hydrocarbons. This suggests that Sol g 2.1 protein may delay the evaporation of the hydrocarbons. Interestingly, the piperidine alkaloids extracted have the highest attraction to the ants. Therefore, the mixture of hydrocarbons and piperidines had a synergistic effect on the trail-following of ants when both were added to the protein. Full article

Wireless sensor networks (WSNs) play a vital role in various fields, but ensuring optimal coverage poses a significant challenge due to the limited energy resources that constrain sensor nodes. To address this issue, this paper presents a novel approach that combines the binary ant colony algorithm (BACA), a variant of ant colony optimization (ACO), with other search optimization algorithms, such as hill climbing (HC) and simulated annealing (SA). The BACA is employed to generate an initial solution by emulating the foraging behavior of ants and the pheromone trails they leave behind in their search for food. However, we acknowledge that the BACA alone may not guarantee the most optimal solution. Subsequently, HC and SA are optimization search algorithms that refine the initial solution obtained by the BACA to find a more enhanced solution. Through extensive simulations and experiments, we demonstrate that our proposed approach results in enhanced coverage and energy-efficient coverage in a two-dimensional (2D) field. Interestingly, our findings reveal that HC consistently outperforms SA, particularly in less complex search spaces, leveraging its robust exploitation approach. Our research contributes valuable insights into optimizing WSN coverage, highlighting the superiority of HC in this context. Finally, we outline promising future research directions that can advance the optimization of WSN coverage. Full article

Ant colony optimization (ACO) is a stochastic optimization algorithm inspired by the foraging behavior of ants. We investigate a simplified computational model of ACO, wherein ants sequentially engage in binary decision-making tasks, leaving pheromone trails contingent upon their choices. The quantity of pheromone left is the number of correct answers. We scrutinize the impact of a salient parameter in the ACO algorithm, specifically, the exponent $\alpha$, which governs the pheromone levels in the stochastic choice function. In the absence of pheromone evaporation, the system is accurately modeled as a multivariate nonlinear Pólya urn, undergoing phase transition as $\alpha$ varies. The probability of selecting the correct answer for each question asymptotically approaches the stable fixed point of the nonlinear Pólya urn. The system exhibits dual stable fixed points for $\alpha \ge {\alpha }_c$ and a singular stable fixed point for $\alpha <{\alpha }_c$ where ${\alpha }_c$ is the critical value. When pheromone evaporates over a time scale $\tau$, the phase transition does not occur and leads to a bimodal stationary distribution of probabilities for $\alpha \ge {\alpha }_c$ and a monomodal distribution for $\alpha <{\alpha }_c$. Full article

Ant colony optimization (ACO) is a well-known class of swarm intelligence algorithms suitable for solving many NP-hard problems. An important component of such algorithms is a record of pheromone trails that reflect colonies’ experiences with previously constructed solutions of the problem instance that is being solved. By using pheromones, the algorithm builds a probabilistic model that is exploited for constructing new and, hopefully, better solutions. Traditionally, there are two different strategies for updating pheromone trails. The best-so-far strategy (global best) is rather greedy and can cause a too-fast convergence of the algorithm toward some suboptimal solutions. The other strategy is named iteration best and it promotes exploration and slower convergence, which is sometimes too slow and lacks focus. To allow better adaptability of ant colony optimization algorithms we use κ-best, max-κ-best, and 1/λ-best strategies that form the entire spectrum of strategies between best-so-far and iteration best and go beyond. Selecting a suitable strategy depends on the type of problem, parameters, heuristic information, and conditions in which the ACO is used. In this research, we use two representative combinatorial NP-hard problems, the symmetric traveling salesman problem (TSP) and the asymmetric traveling salesman problem (ATSP), for which very effective heuristic information is widely known, to empirically analyze the influence of strategies on the algorithmic performance. The experiments are carried out on 45 TSP and 47 ATSP instances by using the MAX-MIN ant system variant of ACO with and without local optimizations, with each problem instance repeated 101 times for 24 different pheromone reinforcement strategies. The results show that, by using adjustable pheromone reinforcement strategies, the MMAS outperformed in a large majority of cases the MMAS with classical strategies. Full article

Ant Colony Optimization (ACO) is a practical and well-studied bio-inspired algorithm to generate feasible solutions for combinatorial optimization problems such as the Traveling Salesman Problem (TSP). ACO is inspired by the foraging behavior of ants, where an ant selects the next city to visit according to the pheromone on the trail and the visibility heuristic (inverse of distance). ACO assigns higher heuristic desirability to the nearest city without considering the issue of returning to the initial city or starting point once all the cities are visited. This study proposes an improved ACO-based method, called ACO with Adaptive Visibility (ACOAV), which intelligently adopts a generalized formula of the visibility heuristic associated with the final destination city. ACOAV uses a new distance metric that includes proximity and eventual destination to select the next city. Including the destination in the metric reduces the tour cost because such adaptation helps to avoid using longer links while returning to the starting city. In addition, partial updates of individual solutions and 3-Opt local search operations are incorporated in the proposed ACOAV. ACOAV is evaluated on a suite of 35 benchmark TSP instances and rigorously compared with ACO. ACOAV generates better solutions for TSPs than ACO, while taking less computational time; such twofold achievements indicate the proficiency of the individual adoption techniques in ACOAV, especially in AV and partial solution update. The performance of ACOAV is also compared with the other ten state-of-the-art bio-inspired methods, including several ACO-based methods. From these evaluations, ACOAV is found as the best one for 29 TSP instances out of 35 instances; among those, optimal solutions have been achieved in 22 instances. Moreover, statistical tests comparing the performance revealed the significance of the proposed ACOAV over the considered bio-inspired methods. Full article

Ants belonging to the Formica rufa group build large nest mounds, which aid their survival during severe winters. We investigated whether different environmental features of the habitats affected the nest mound shape and the population structure. We assessed the shape of all the nest mounds and mapped inter-nest trails connecting mounds for three imported populations of Formica paralugubris in three forest habitats: fir-dominated, beech-dominated, and a mixture of fir and beech. Single-nest mounds were averagely smaller and flatter in the beech-dominated forest, probably because of lighter building materials. Nonetheless, by summing the volumes of all interconnected nests, the size was similar among all three sites. In fir- and beech-dominated forests, large nests were also central in the networks, suggesting a central place foraging model with these nests as reference. We finally performed aggression tests, and found that aggressiveness was significantly higher among nests belonging to the same population than between populations. The results highlight the plasticity of the species to adapt nest and colony structure to different environments. Additionally, it appears that none of these populations is unicolonial, as observed in various alpine sites, there and the observed patterns of aggression are coherent with the ‘nasty neighbor’ effect. Full article

Ants (Hymenoptera: Formicidae), especially the Argentine ant, Linepithema humile (Mayr), can be significant nuisance pests in urban and suburban environments. Conventional interventions have primarily relied on the use of chemical insecticides, namely fipronil and bifenthrin, applied as residual, contact treatments around the outside perimeter of infested structures. Despite tightening regulation limiting the scope of insecticide applications in urban settings, dependence on these products to manage ants continues, resulting in significant water contamination. The U.S. EPA, in response, has further restricted the use patterns of many insecticides used for ant control in professional and over-the-counter markets. The purpose of this review is to summarize the relevant literature associated with controlling nuisance pest ants, with emphasis on L. humile, without the use of liquid broadcast applications of EPA-registered insecticides while focusing on low-impact, alternative (to broadcast applications) pest control methods. Specific subsections include Trail Pheromone; Use of Behavior-Modifying Chemicals; Mass Trapping; Hydrogels, “Virtual” Baiting, and Exceedingly-Low Bait Concentrations; Food Source Reduction; Deterrents; and RNA Interference (RNAi). Full article

This work presents a novel Best-Worst Ant System (BWAS) based algorithm to settle the Traveling Salesman Problem (TSP). The researchers has been involved in ordinary Ant Colony Optimization (ACO) technique for TSP due to its versatile and easily adaptable nature. However, additional potential improvement in the arrangement way decrease is yet possible in this approach. In this paper BWAS based incorporated arrangement as a high level type of ACO to upgrade the exhibition of the TSP arrangement is proposed. In addition, a novel approach, based on hybrid Particle Swarm Optimization (PSO) and ACO (BWAS) has also been introduced in this work. The presentation measurements of arrangement quality and assembly time have been utilized in this work and proposed algorithm is tried against various standard test sets to examine the upgrade in search capacity. The outcomes for TSP arrangement show that initial trail setup for the best particle can result in shortening the accumulated process of the optimization by a considerable amount. The exhibition of the mathematical test shows the viability of the proposed calculation over regular ACO and PSO-ACO based strategies. Full article