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This paper presents an adaptation of the flying ant colony optimization (FACO) algorithm to solve the traveling salesman problem (TSP). This new modification is called dynamic flying ant colony optimization (DFACO). FACO was originally proposed to solve the quality of service (QoS)-aware web service selection problem. Many researchers have addressed the TSP, but most solutions could not avoid the stagnation problem. In FACO, a flying ant deposits a pheromone by injecting it from a distance; therefore, not only the nodes on the path but also the neighboring nodes receive the pheromone. The amount of pheromone a neighboring node receives is inversely proportional to the distance between it and the node on the path. In this work, we modified the FACO algorithm to make it suitable for TSP in several ways. For example, the number of neighboring nodes that received pheromones varied depending on the quality of the solution compared to the rest of the solutions. This helped to balance the exploration and exploitation strategies. We also embedded the 3-Opt algorithm to improve the solution by mitigating the effect of the stagnation problem. Moreover, the colony contained a combination of regular and flying ants. These modifications aim to help the DFACO algorithm obtain better solutions in less processing time and avoid getting stuck in local minima. This work compared DFACO with (1) ACO and five different methods using 24 TSP datasets and (2) parallel ACO (PACO)-3Opt using 22 TSP datasets. The empirical results showed that DFACO achieved the best results compared with ACO and the five different methods for most of the datasets (23 out of 24) in terms of the quality of the solutions. Further, it achieved better results compared with PACO-3Opt for most of the datasets (20 out of 21) in terms of solution quality and execution time.

As one suitable optimization method implementing computational intelligence, ant colony optimization (ACO) can be used to solve the traveling salesman problem (TSP). However, traditional ACO has many shortcomings, including slow convergence and low efficiency. By enlarging the ants’ search space and diversifying the potential solutions, a new ACO algorithm is proposed. In this new algorithm, to diversify the solution space, a strategy of combining pairs of searching ants is used. Additionally, to reduce the influence of having a limited number of meeting ants, a threshold constant is introduced. Based on applying the algorithm to 20 typical TSPs, the performance of the new algorithm is verified to be good. Moreover, by comparison with 16 state-of-the-art algorithms, the results show that the proposed new algorithm is a highly suitable method to solve the TSP, and its performance is better than those of most algorithms. Finally, by solving eight TSPs, the good performance of the new algorithm has been analyzed more comprehensively by comparison with that of the typical traditional ACO. The results show that the new algorithm can attain a better solution with higher accuracy and less effort.

The research introduces a Pheromone-based Ant Trusted Routing Algorithm (PATRA), aimed at improving routing efficiency and security in Wireless Sensor Networks (WSN). The approach will combine Ant-Colony Optimization (ACO) with reputation-based mechanisms to ensure trusted data delivery through the selection of more trustworthy and energy-efficient nodes. Packet Delivery Ratio (PDR), Energy Consumption, Packet Loss Rate, and the number of received packets are considered for the performance metrics that are observed through extensive simulations with a range of environments, including the possibility of malicious nodes. The results indicate that PATRA consistently outperforms conventional approaches like Quality of Service - Particle Swarm Optimization (QOS-PSO), Ant Colony Optimization Routing Control (ACORC), and Trust-Aware Node Activity Routing Protocol (TANARP) by maintaining a high PDR, reduced energy consumption, and lower packet loss rates with a maximization of received packets. These further demonstrate that PATRA possesses robustness regarding the impact of malicious nodes and network lifetime. The simulation experiments also confirm that the proposed approach outperforms the previous approaches by a large margin in security, efficiency, and reliability, and is thus a promising approach to be employed for secure and energy-efficient WSNs.

Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called aging-based ant colony optimization (ABACO). The ABACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments.

To reduce the security risks and guide management pressure of \"mountain-type\" scenic areas with steep and changeable topography, this paper combines the idea of swarm security intelligence and proposes a swarm security intelligent dynamic networking algorithm based on multiple constraints to ensure the swarm safety of the tour group and relieve the management pressure of tour guide through the dynamic group and the self-discovery after leaving the group. Based on multiple constraints such as signal strength, power consumption and fault probability etc, the algorithm optimizes the network topology in real-time, which can effectively reduce the high false-positive rate o f leaving the group due to node failure. Secondly, the algorithm constructs the Spf constrained fitness function and introduces the ant colony optimization algorithm to avoid the situation that the optimal routing path falls into the local optimal solution. Finally, the algorithm converts the adjacency matrix composed of routing table and node connection signals into areachable matrix to judge whether the member has left the group and reduce the false-negative rate of group loss in the case of multi-node connection. Experimental results show that the algorithm has good stability and robustness in mobile wireless adhoc networks

Purpose: The objective of this work is to fulfil a strategic requirement in Egypt’s agriculture industry by establishing a fresh produce supply chain network (SCN) that manages the collection, processing, packaging, and distribution of products.Design/methodology/approach: A two-phase approach is proposed. In the first, a network of food aggregation hubs is strategically located across the country for the collection, consolidation, and distribution of products. This is accomplished by modeling and solving a cost minimization dynamic facility location-allocation (FLA) problem using a hybrid binary particle swarm optimization (BPSO) algorithm. The second phase of the approach is to complement the hub FLA decision with optimal fleet size, transportation schedules, and routing decisions. This is achieved by solving the split-delivery vehicle routing problem (SDVRP) using a hybrid ant-colony optimization (ACO) algorithm, considering positioning loading constraints, and shelf-lives of products.Findings: There is a strong correlation between the geographical locations and capacities of the established hubs, and the proximity of supply points and the populations in the demand areas. In addition, accounting for spoilage of products has a significant effect on network design, and collection and distribution decisions.Practical implications: Establishment of the intended SCN can reduce the proportion of wasted product during transit, and improve the quality of the delivered product.Social implications: Establishment of the SCN will increase the exposure of small farmers to wider markets, and hence their return and standard of living, and potentially reduce the prices for the final customer.Originality/value: This study is the first attempt to establish an efficient fresh produce supply chain network in Egypt. In addition, the proposed solution approach considered a multitude of problem characteristics, simultaneously for the first time. 

Web Service Composition (WSC) provides a flexible framework for integrating independent web services to satisfy complex user requirements. WSC aims to choose the best web service from a set of candidates. The candidates have the same functionality and different non-functional criteria such as Quality of Service (QoS). In this work, the authors propose an ant-inspired algorithm for such problem. They named it Flying Ant Colony Optimization (FACO). Flying ants inject pheromone not only on the nodes on their paths but also on neighboring nodes increasing their chances of being explored in future iterations. The amount of pheromone deposited on these neighboring nodes is inversely proportional to the distance between them and the nodes on the path. The authors believe that by depositing pheromone on neighboring nodes, FACO may consider a more diverse population of solutions, which may avoid stagnation. The empirical experiments show that FACO outperform Ant Colony Optimization (ACO) for the WSC problem, in terms of the quality of solutions but it requires slightly more execution time.

To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in ant colony optimization (ACO) algorithm in solving complex optimization problems, the chaotic optimization method, multi-population collaborative strategy and adaptive control parameters are introduced into the GA and ACO algorithm to propose a genetic and ant colony adaptive collaborative optimization (MGACACO) algorithm for solving complex optimization problems. The proposed MGACACO algorithm makes use of the exploration capability of GA and stochastic capability of ACO algorithm. In the proposed MGACACO algorithm, the multi-population strategy is used to realize the information exchange and cooperation among the various populations. The chaotic optimization method is used to overcome long search time, avoid falling into the local extremum and improve the search accuracy. The adaptive control parameters is used to make relatively uniform pheromone distribution, effectively solve the contradiction between expanding search and finding optimal solution. The collaborative strategy is used to dynamically balance the global ability and local search ability, and improve the convergence speed. Finally, various scale TSP are selected to verify the effectiveness of the proposed MGACACO algorithm. The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.

Load Balancing (LB) is one of the most important tasks required to maximize network performance, scalability and robustness. Nowadays, with the emergence of Software-Defined Networking (SDN), LB for SDN has become a very important issue. SDN decouples the control plane from the data forwarding plane to implement centralized control of the whole network. LB assigns the network traffic to the resources in such a way that no one resource is overloaded and therefore the overall performance is maximized. The Ant Colony Optimization (ACO) algorithm has been recognized to be effective for LB of SDN among several existing optimization algorithms. The convergence latency and searching optimal solution are the key criteria of ACO. In this paper, a novel dynamic LB scheme that integrates genetic algorithm (GA) with ACO for further enhancing the performance of SDN is proposed. It capitalizes the merit of fast global search of GA and efficient search of an optimal solution of ACO. Computer simulation results show that the proposed scheme substantially improves the Round Robin and ACO algorithm in terms of the rate of searching optimal path, round trip time, and packet loss rate.

Wireless Sensor Networks (WSNs) monitor and gather environmental data by interconnecting numerous sensor nodes spread across space via wireless communication. These nodes operate on battery power, which depletes over time, thereby limiting the network’s operational lifespan. This energy constraint significantly impacts the overall longevity of the network. The primary focus of the work is to reduce energy consumption and increase the network’s lifespan. To this end, WSNs currently make extensive use of routing and clustering algorithms. This work selects an optimal Cluster Head (CH) from a set of nodes using a combination of hybrid Ant Colony Optimization (ACO) and the Improved Social Spider Cluster Optimization Algorithm (ISSOA). The selection process takes into account a number of variables, such as the nodes' residual energy, their degree and centrality, their proximity to nearby nodes, and their distance from the Base Station (BS). Furthermore, we use an optics-inspired optimization (OIO) algorithm to determine the path between the chosen CH and the BS. In order to ensure effective data transmission throughout the network, this algorithm optimizes the path based on variables including distance, node degrees, and the residual energy of nodes along the route. The simulation results show that the proposed ACO-ISSOA method significantly improves several Quality of Service (QoS) parameters. The proposed method works better than other algorithms like Low Energy Adaptive Central Hierarchical Clustering (LEACH-C), Multiple-Weight LEACH (MW-LEACH), Hybrid Genetic Algorithm with Particle Swarm Optimization (GA-PSO), and ACO-based Hierarchical Clustering (ACOHC). The ACO-ISSOA protocol improves network lifetime (5700 rounds), throughput (99%), PDR (99.5%), energy consumption (56 mJ), and execution time (45 s) for CH selection. When compared to other algorithms, the hybrid ACO-ISSO algorithm outperforms them.