Chaos-Ant Colony Algorithm and its application in continuous space optimization

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.

Inverse transient radiation analysis in one-dimensional participating slab using improved Ant Colony Optimization algorithms

In the current research field of mobile robots, optimal path planning is a hot topic. Ant colony algorithm is diffusely used in mobile robot path planning because of its its positive feedback, distributed and so on. However, traditional basic ant colony algorithm has some disadvantages, such as easy local convergence, slow search convergence rate and low efficiency. To solve these problems, this paper proposes an improved ant colony algorithm. Based on the algorithm principle, a new method for global pheromone initialization is put forward by improving and optimizing the pheromone mechanism, and there is a certain probability to carry out “variation” operation in each search iteration process. Meanwhile, the calculation method of heuristic function is improved to reduce the possibility of premature convergence of the algorithm falling into local optimum. The two-dimensional space model of mobile robot is created by grid method. Then they are simulated and tested respectively, and the test results are compared and analyzed. The experimental results show that the improved algorithm can obviously optimize the defects of the original algorithm, which not only can plan the path faster, but also can plan the better path, and the search efficiency has been significantly improved.

Mass customization necessitates increased product variety at the customers’ end but comparatively lesser part variety at the manufacturer’s end. Product platform concepts have been successful to achieve this goal at large. One of the popular methods for product platform formation is to scale one or more design variables called the scaling variables. Effective optimization methods are needed to identify proper values of the scaling variables. This paper presents a graph-based optimization method called the scalable platforms using ant colony optimization (SPACO) method for identifying appropriate values of the scaling variables. In the graph-based representation, each node signifies a sub-range of values for a design variable. This application includes the concept of multiplicity in node selection because there are multiple nodes corresponding to the discretized values of a given design variable. In the SPACO method, the overall decision is a result of the cumulative decisions, made by simple computing agents called the ants, over a number of iterations. The space search technique initially starts as a random search technique over the entire search space and progressively turns into an autocatalytic (positive feedback) probabilistic search technique as the solution matures. We use a family of universal electric motors, widely cited in the literature, to test the effectiveness of the proposed method. Our simulation results, when compared to the results reported in the literature, prove that SPACO method is a viable optimization method for determining the values of design variables for scalable platforms.

The traveling salesman problem (TSP) is an NP-hard problem. Thus far, a large number of researchers have proposed different ant colony optimization (ACO) algorithms to solve the TSP. These algorithms inevitably encounter problems such as long convergence time and the tendency to easily fall into local optima. On the basis of the ACO algorithm, this study proposes a dynamic adaptive ACO algorithm (DAACO). DAACO realizes the diversity of initialization of the ACO algorithm by dynamically determining the number of ants to be prevented from falling into local optimization. DAACO also adopts a hybrid local selection strategy to increase the quality of ant optimization and reduce the optimization time. Among the 20 instances of the TSPLIB dataset, the DAACO algorithm obtains 19 optimal values, and the solutions of 10 instances are better than those of other algorithms. The experimental results on the TSPLIB dataset show that the DAACO algorithm has obvious advantages in terms of convergence time, solution quality, and average value relative to existing state-of-the-art ACO algorithms.

Mobile robot path planning using Ant Colony Optimization (ACO) has the disadvantages of slow convergence, local optima, and unsmooth paths because of fixed heuristics and constant pheromone updating. In this paper, Self-Adaptive Risk-Aware Bidirectional updating ACO (SAR-BACO) is proposed with three improvements: (1) composite heuristic incorporating target attraction, obstacle repulsion and direction consistency to minimize early blind searching; (2) dynamic pheromone updating based on solution quality and number of iterations to balance exploration and exploitation; (3) triangular pruning to remove redundant turning points and become smoother. Theoretical analysis verifies convergence. Our experimental results on grids up to 50 × 50 demonstrate that SAR-BACO performs much better than classical and heuristic-improved algorithms with respect to the length of a path, convergence rate, smoothness and efficiency. Using SAR-BACO on a 50 × 50 map, the path lengths, convergence iterations and turning points decreased by 60.68%, 48.96%, and 96.00% respectively compared to Basic ACO (after triangular pruning, values averaged over 50 runs). The framework provides a generalizable solution to autonomous navigation with the need to consider both search efficiency and path executability.

Abstract. With the advancement of urbanization, urban-rural public transport issues have become one of the most critical issues in urban development. The paper makes a detailed study on the optimization of urban-rural bus routes in Erqi District of Zhengzhou, China. The factors of bus stop selection are analyzed, and the three categories, including traffic road condition factors, economic benefit factors and waiting number factors, are mainly considered. The analytic hierarchy process is used to determine 35 specific objectives of urban-rural bus stop optimization, 20 of which are selected for simulation experiment with large weight. Then the ant colony optimization (ACO) algorithm in path optimization is analyzed, which has the following two advantages. First, the global pheromone update is combined with the local pheromone update to enhance the algorithm's optimization ability and convergence speed. Second, through the method of spatial contraction transformation, the ant constructs a solution to reduce the number of construction steps and speed up the operation. Based on the actual analysis of urban-rural public transportation in the Erqi District of Zhengzhou, a simulation experiment is executed to show that the ACO algorithm is able to find out the optimal path, which is 15.1% shorter than the ant colony system (ACS) algorithm. The ACO algorithm improved path planning has good time effectiveness and path practicability.

Text categorisation (TC) is the task of assigning predefined categories to text. Most of the machine learning algorithms are sensitive to the features fed into it. Feature selection (FS) is an important preprocessing step to remove redundant and irrelevant terms from the training corpus. This paper proposes an ant colony optimisation (ACO) algorithm to select features for categorising longer documents to closely related categories. Heuristic value for each word is computed by the statistical dependency of the term to a category and its compactness value. Experiments were conducted with documents from 20newsgroup and Reuters-21578 benchmarks. The selected features were fed into the Naïve Bayes classifier. It was observed that the performance of the classifier improves with the features selected by the proposed method. The processes involved in algorithm are time intensive and demand parallelism. Hence, the ACO algorithm was parallelised using the MapReduce programming model of Hadoop.

In last two decades, Ant colony algorithm got extensive application in combinatorial optimization, function optimization and other fields. Ant colony algorithm is easy to fall into local optimum. A novel estimation of distribution algorithm by fusion improvement on ant colony algorithm and PBIL estimation of distribution algorithm is proposed. The algorithm introduce probability distribution model of PBIL algorithm to guide route choice, which greatly improves the faults that positive feedback mechanism of pheromone. Although the hybrid ant colony algorithm has achieved good results, this is just the preliminary attempt of distributed estimation algorithm combined with ant colony algorithm. Probability distribution model of other distribution estimation algorithm can also be used to guide the choice of ant colony optimal path.

The chapter discusses the application of Opposition-Based Optimization (OBO) to ant algorithms. Ant Colony Optimization (ACO) is a powerful optimization technique that has been used to solve many complex problems. Despite its successes, ACO is not a perfect algorithm: it can remain trapped in local optima, miss a portion of the solution space or, in some cases, it can be slow to converge. Thus, we were motivated to improve the accuracy and convergence of the current algorithm by extending it with the concept of OBO. In the case of ACO, the application of opposition can be challenging because ACO usually optimizes using a graph representation of problems, where the opposite of solutions and partial components of the solutions are not clearly defined. The chapter presents two types of opposition-based extensions to the ant algorithm. The first type, called Opposite Pheromone per Node (OPN), involves a modification to the construction phase of the algorithm which affects the decisions of the ants by altering the pheromone values used in the decision. Basically, there is an opposite rate that determines the frequency at which opposite pheromone will be used in the construction step. The second method, Opposite Pheromone Update (OPU), involves an extension to the update phase of the algorithm that performs additional updates to the pheromone content of opposite decisions. The opposition-based approaches were tested using the Travelling Salesman Problem (TSP) and the Grid World Problem (GWP). Overall, the application of some fundamental opposition concepts led to encouraging results in the TSP and the GWP. OPN led to some accuracy improvements and OPU demonstrated significant speed-ups. However, further work is necessary to fully evaluate the benefits of opposition. Theoretical work involving the application of opposition to graphs is necessary, specifically in establishing the ‘opposite graph’.

Aiming to the demand and characteristics of vehicle scheduling optimization problem in emergency rescue, this paper establishes a multi-objective optimization model, which takes minimizing variable bidirectional distance, path risk and cost as the optimization target. To avoid the prematurely falling into local optimization of ant colony system(ACS)algorithm, and to improve the algorithm adaptability, computational efficiency and the quality of optimal solution, this paper proposed and realized a chaos-based improved ant colony system algorithm, which can overall updates the chaos disturbance to pheromone. Simulation results show that the algorithm is feasible, which can well meet the demand of vehicle scheduling optimization in emergency rescue.

The ant colony optimization (ACO) algorithm is improved and further integrated with the immune algorithm (IA) to address its problems, such as slow convergence, local optimum, and premature convergence in the path planning. An algorithm integrating IA and improved ant colony optimization (IACO) is, therefore, put forward to realize the optimal planning of global path for an unmanned surface vehicle (USV). First, the ACO algorithm was improved in three aspects, that is, generation of initial pheromones, transition probability, and update of pheromones. The proposed IA-IACO algorithm combined the advantages of IA and IACO, sped up the convergence, and enhanced the optimization capability and operational efficiency. Second, the IA-IACO algorithm was designed and applied in the global path planning of an unmanned surface vehicle, achieving great global optimization and convergence. Finally, a path smoothing algorithm was devised to achieve the implementable, economic, and stable path while guaranteeing the safe navigation of the USV. A simulation test was carried out to prove the effectiveness and superiority of the designed global path planning algorithm in the practical engineering.

In order to improve the search result and low evolution speed, and avoid the tendency towards stagnation and falling into the local optimum of ant colony optimization(ACO) in solving the complex function, the traditional ant colony optimization algorithm is analyzed in detail, an improved ant colony optimization(IWSMACO) algorithm based on information weight factor and supervisory mechanism is proposed in this paper. In the proposed IWSMACO algorithm, the information weight factor is added to the path selection and pheromone adjustment mechanisms in order to dynamically adjust path selection probability and randomly select the behavior rules for further intelligentializing the ant colony. The supervisory mechanism added the dynamic convergence criterion of supervisory distance and used the optimal pheromone update strategy to self-adaptively select the excellent ants for updating the pheromone trails, and improve the solution qualities of each iteration, better guide the later ants for learning. Finally, the proposed IWSMACO algorithm is carried out by 12 TSP instances. The simulation experiment results show that the proposed IWSMACO algorithm can not only avoid falling into the local optimum, but also enhance the convergence speed. And it takes on remarkable optimized ability and higher search accuracy.

As an extension of the classical job shop scheduling problem, the flexible job shop scheduling problem (FJSP) plays an important role in real production systems. In FJSP, an operation is allowed to be processed on more than one alternative machine. It has been proven to be a strongly NP-hard problem. Ant colony optimization (ACO) has been proven to be an efficient approach for dealing with FJSP. However, the basic ACO has two main disadvantages including low computational efficiency and local optimum. In order to overcome these two disadvantages, an improved ant colony optimization (IACO) is proposed to optimize the makespan for FJSP. The following aspects are done on our improved ant colony optimization algorithm: select machine rule problems, initialize uniform distributed mechanism for ants, change pheromone’s guiding mechanism, select node method, and update pheromone’s mechanism. An actual production instance and two sets of well-known benchmark instances are tested and comparisons with some other approaches verify the effectiveness of the proposed IACO. The results reveal that our proposed IACO can provide better solution in a reasonable computational time.

Unmanned Aerial Vehicles (UAVs) are indispensable assets for missions in dynamic and complex environments, requiring highly efficient path planning that simultaneously optimizes the often-conflicting objectives of minimizing flight distance, energy consumption, and mission time. While Ant Colony Optimization (ACO) is a recognized and effective metaheuristic for this domain, its performance is significantly constrained by a static, empirically-derived pheromone update mechanism, which prevents the algorithm from adaptively learning or optimally managing the search process. To overcome this critical limitation, this study introduces a novel DRL-Assisted ACO framework where a Deep Reinforcement Learning (DRL) agent is seamlessly integrated with the ACO to strategically determine the optimal paths under multi-objective constraints. This intelligent agent is tasked with learning the optimal, mission-specific pheromone update strategy. It achieves this by observing the performance of generated paths and receiving a sophisticated reward signal meticulously derived from the Analytic Hierarchy Process (AHP), which systematically weights the mission objectives. Validated through a simulated case study conducted in Khartoum State, Su-dan, the DRL-Assisted ACO approach has demonstrably achieved superior performance, exhibiting marked gains in convergence speed and generating paths with a significantly higher overall multi-objective utility score, thereby delivering a robust and adaptive solution essential for high-stakes autonomous UAV operations.