Optimization of mobile flow routing in a wireless sensor network using heuristic algorithms

Control parameter optimisation using the evidence framework for the ant colony optimisation algorithm

These days computers deals with highly intricate problems. This paper also discusses such kind of complex problems called Constraint Satisfaction Problems (CSP). These problems have a set of variables, domain from which a variable takes its value and a set of constraints applied on these variables. For example N-Queen problem, timetabling problem, scheduling problem etc. are CSPs. In practical scenario, it is unlikely to obtain a solution that satisfies all constraints or most of the constraints. Such a solution is an exact solution. Even if an exact algorithm can be developed its time or space complexity may turnout unacceptable. In reality, it is often sufficient to find an approximate or partial solution to such NP problems using heuristic algorithms. Heuristic methods are used to speed up the process of finding a satisfactory solution, where an exhaustive search is impractical; hence resulting in guaranteed and approximate solutions. This paper proposes an efficient hybrid solution for standard N-Queen problem using Ant Colony Optimization and Genetic Algorithm. It also compares the performances of classical backtrack and brute force methods and heuristic methods, Simulated annealing and Genetic algorithm on N-Queen problem.

Traditional wireless sensor networks (WSNs) with one static sink node suffer from the well-known hot spot problem, that of sensor nodes near the static sink bear more traffic load than outlying nodes. Thus, the overall network lifetime is reduced due to the fact some nodes deplete their energy reserves much faster compared to the rest. Recently, adopting sink mobility has been considered as a good strategy to overcome the hot spot problem. Mobile sink(s) physically move within the network and communicate with selected nodes, such as cluster heads (CHs), to perform direct data collection through short-range communications that requires no routing. Finding an optimal mobility trajectory for the mobile sink is critical in order to achieve energy efficiency. Taking hints from nature, the ant colony optimization (ACO) algorithm has been seen as a good solution to finding an optimal traversal path. Whereas the traditional ACO algorithm will guide ants to take a small step to the next node using current information, over time they will deviate from the target. Likewise, a mobile sink may communicate with selected node for a relatively long time making the traditional ACO algorithm delays not suitable for high real-time WSNs applications. In this paper, we propose an improved ACO algorithm approach for WSNs that use mobile sinks by considering CH distances. In this research, the network is divided into several clusters and each cluster has one CH. While the distance between CHs is considered under the traditional ACO algorithm, the mobile sink node finds an optimal mobility trajectory to communicate with CHs under our improved ACO algorithm. Simulation results show that the proposed algorithm can significantly improve wireless sensor network performance compared to other routing algorithms.

In this paper we present a simple heuristic algorithm to find high-quality, feasible solutions, for the traveling salesman problem (TSP). We hypothesize, that the quality of the initial solution provided by the proposed heuristic will improve the performance of the subsequent algorithm in terms of number of iterations required to reach a certain level TSP solution. The proposed heuristic does not attempt to compete against known TSP algorithms and heuristics, but instead, should be considered to serve as a “pre-processor”. The method provides a simple framework for testing new node selection and neighborhood rules. The cost matrix of origin and destination pairs is processed in a systematic way starting from a principal diagonal matrix element to find a feasible TSP tour. The matrix reduction, systematic moves in rows and columns, systematic elimination of rows and columns from further consideration, and the “reserved” column declaration, assure that the resulting sequence of nodes and edges forms a complete TSP tour. The process can be repeated from each principal diagonal element. The best TSP tour found can then be used, for example, as an input to another algorithm (e.g. the TABU search, simulated annealing, ant colony optimization, nearest neighbor, or another heuristic) to attempt to improve the tour further. It should be noted, that the proposed technique can also be used for testing of presence of cycles of a proposed solution provided by another algorithm. While the goal of the heuristic algorithm is to attempt to find the optimum tour, optimality cannot be guaranteed.

Gathering data from sensors, mobile sink has been adopted in wireless sensor networks (WSN) and wireless sensor and actor network(WSAN) to achieve higher efficiency. With the help of mobile sink, sensors could attain a much longer lifetime than the past. However, mobile sink introduces new challenges such as large data gathering latency to WSN/WSAN. A lot of research efforts have been devoted to reduce the data gathering time. Cooperated with a novel partition algorithm, a concise and efficient data gathering scheme is proposed here. A given area can be divided into several zones with balanced data gathering latency. By modeling the partitioning problem as a Traveling Salesman Problem (TSP), an algorithm is designed to balance the data gathering latency among all the zones. Then mobile sinks are assigned to these zones separately. The data could be gathered by these mobile sinks parallel thereupon. Extensive simulations are carried out to evaluate our proposed data gathering scheme. Different distribution patterns are considered. Effectiveness of our proposed data gathering scheme is proved by the simulation results.

A new hybrid method based on Particle Swarm Optimization, Ant Colony Optimization and 3-Opt algorithms for Traveling Salesman Problem

This paper investigates optimum path planning for CNC drilling machines for a special class of products that involve a large number of holes arranged in a rectangular matrix. Examples of such products include boiler plates, drum and trammel screens, connection flanges in steel structures, food-processing separators, as well as certain portions of printed circuit boards. While most commercial CAD software packages include modules that allow for automated generation of the CNC code, the tool path planning generated from the commercial CAD software is often not fully optimised in terms of the tool travel distance, and ultimately, the total machining time. This is mainly due to the fact that minimisation of the tool travel distance is a travelling salesman problem (TSP). The TSP is a hard problem in the discrete programming context with no known general solution that can be obtained in polynomial time. Several heuristic optimisation algorithms have been applied in the literature to the TSP, with varying levels of success. Among the most successful algorithms for TSP is the ant colony optimisation (ACO) algorithm, which mimics the behaviour of ants in nature. The research in this paper applies the ACO algorithm to the path planning of a CNC drilling tool between holes in a rectangular matrix. In order to take advantage of the rectangular layout of the holes, two modifications to the basic ACO algorithm are proposed. Simulation case studies show that the average discovered path via the modified ACO algorithms exhibit significant reduction in the total tool travel distance compared to the basic ACO algorithm or a typical genetic algorithm.

Two kinds of sensors will be discussed in some sort of wireless sensor networks (WSNs). One is named a normal sensor (called A_nodes) with fixed initial energy, which can get perception data from the surrounding environment and have functions of storage and forwarding. The other is named relay sensor (called B_node) with sufficient energy, which only can store data and forward data. Cluster heads (called A_heads) are chosen among A_nodes by probability mode to create clusters first. Local adjustments would be done inter-cluster. Then the sink is selected as the root and a backbone shortest path tree with hops limited is built dynamically from A_heads, B_Nodes and sink. Inside the backbone shortest path aggregation tree, two steps are done. The first step, select some B_node as cluster head (denoted as B_node) and make local adjustment intra-clusters if it is possible. The second step, adjust the shortest path aggregation tree. By using both particle swarm optimization (PSO) and ant colony optimization (ACO), a traveling salesman problem (TSP) cycle with shorter path length is obtained (this method is noted as TSP_PSO_ACO). This TSP cycle consists of all nodes in the aggregation backbone tree. Along this cycle, energy is offered to nodes except sink. The above strategy would be discussed for its feasibility and time complexity. The experimental results imply that the energy consumption is reduced by using local adjustment. The relative failure rate of nodes can be reduced in the later stage of WSN survival. Offering energy to a small number of nodes several times has no obvious impact on the overall energy consumption and survival time of WSN. The case with some small-sized obstacles in WSN area is also discussed. There are little affections on TSP cycles both from results on theoretical analysis and simulation if there are some obstacles with small size in WSN area.

Heuristic smoothing ant colony optimization with differential information for the traveling salesman problem

Dellat: Delivery Latency Minimization in Wireless Sensor Networks with Mobile Sink

Ant colony optimization (ACO) algorithm is a metaheuristic inspired by the behavior of real ants in their search for the shortest path to food sources. The ACO algorithm takes on these characteristics of robust, positive feedback distributed computing, easy fusing with other algorithms. But the basic ACO algorithm has some deficiencies of premature and stagnation phenomenon in the evolution process, and is easily trapped into local optimal solution. And it is difficult to explore other solutions in the neighbor space. So a improved ACO(DPSEMACO) algorithm based on dual population strategy, bi-directional dynamic adjust evaporation factor strategy of the pheromone and parallel strategy is proposed to solve the traveling salesman problem(TSP). In the DPSEMACO algorithm, the ants are divided into the two subpopulations by borrowing the mutual cooperation mechanism of biological community, which evolve separately and exchange information timely. The bi-directional dynamic adjusting evaporation factor strategy of the pheromone is used to change the corresponding path pheromone of different subpopulations in order to avoid to trap into a local optimum. The parallel strategy can avoid falling into a local optimum. And the DPSEMACO algorithm can expand the search space and improve the overall searching performance by repeated changing the pheromone of the each subpopulation and adaptive adjusting evaporation factor. Finally, in order to prove the optimization performance of the proposed DPSEMACO algorithm, some classic TSP instances are selected from the TSPLIB in this paper. And some existing methods are selected to compare the optimization performance with the proposed DPSEMACO algorithm. The experimental results demonstrate that the proposed DPSEMACO algorithm is feasible and effective in solving TSP, and takes on a good global searching ability and high convergence speed.

Configuring heterogeneous wireless sensor networks under quality-of-service constraints

In this paper, we prompt a new multi-dimensional algoithm to solve the traveling salesman problem based on the ant colony optimization algorithm and genetic algorithm.Ant Colony Optimization (ACO) is a heuristic algorithm which has been proven a successful technique and applied to a number of combinatorial optimization (CO) problems.The traveling salesman problem (TSP) is one of the most important combinatorial problems.ACO is taken as one of the high performance computing methods for TSP.It still has some drawbacks such as stagnation behavior, long computational time, and premature convergence problem of the basic ACO algorithm on TSP.Those problems will be more obvious when the considered problems size increases.The proposed system based on basic ACO algorithm with well distribution strategy and information entropy which is conducted on the configuration strategy for updating the heuristic parameter in ACO to improve the performance in solving TSP.Then, ACO for TSP has been improved by incorporating local optimization heuristic.Algorithms are tested on benchmark problems from TSPLIB and test results are presented.From our experiments, the proposed algorithm has better performance than ACO algorithm.

Ant Colony Optimization (ACO) and Ant Colony Clustering (ACC), inspired by the decentralized foraging behavior of ants, have emerged as highly effective metaheuristics for addressing complex, large-scale optimization and unsupervised learning challenges in modern computing. This article reviews contemporary research from 2016-2025, synthesizing the application and enhancement of these algorithms across three critical domains. Key findings demonstrate ACO’s capability to achieve significant resource efficiency in Network Optimization, including a reported 9.646% reduction in node power usage in Wireless Sensor Networks (WSN) and highly competitive latency minimization in Edge Computing. Furthermore, ACC is validated as a superior solution for Dynamic Data Stream Mining, utilizing memory-efficient, single-scan methods for real-time clustering. In specialized applications, such as the Traveling Salesman Problem (TSP) and Bio-Computational Analysis, hybrid ACO variants successfully overcome limitations like premature convergence and statistical bias by integrating community network analysis and novel heuristic criteria. The evidence confirms that customized ACO and ACC algorithms provide scalable, self-organizing solutions that outperform traditional methods, driving forward resource-aware and intelligent distributed systems.

The travelling salesman problem (TSP) is perhaps the most researched problem in the field of Computer Science and Operations. It is a known NP-hard problem and has significant practical applications in a variety of areas, such as logistics, planning, and scheduling. Route optimisation not only improves the overall profitability of a logistic centre but also reduces greenhouse gas emissions by minimising the distance travelled. In this article, we propose a simple and improved heuristic algorithm named 2-Opt++, which solves symmetric TSP problems using an enhanced 2-Opt local search technique, to generate better results. As with 2-Opt, our proposed method can also be applied to the Vehicle Routing Problem (VRP), with minor modifications. We have compared our technique with six existing algorithms, namely ruin and recreate, nearest neighbour, genetic algorithm, simulated annealing, Tabu search, and ant colony optimisation. Furthermore, to allow for the complexity of larger TSP instances, we have used a graph compression/candidate list technique that helps in reducing the computational complexity and time. The comprehensive empirical evaluation carried out for this research work shows the efficacy of the 2-Opt++ algorithm as it outperforms the other well-known algorithms in terms of the error margin, execution time, and time of convergence.