Constraint Handling and Stochastic Ranking in ACO

Evolutionary Algorithms have been widely used to solve difficult constrained optimization problems. However, Evolutionary algorithms are intrinsically unconstrained optimization techniques. Constraint handling is mostly incorporated additionally and its choice has great bearing on the quality of the solution. Stochastic Ranking was introduced as an improvement over Feasibility Rules for handling constraints in Evolutionary Optimization. It is widely believed that stochastic ranking is currently the best-known technique for handling constraints. However, a fair comparative study has never been attempted in the literature, where by the performance of both the constraint handling technique is compared on the same Evolutionary Algorithm. This paper fairly compares the performance of both the constraint handling techniques on the same Evolutionary Algorithm over a set of parameters like feasibility rate, successful run, success rate and success performance in addition to objective function value and number of function evaluations. The results put a question mark over the belief that Feasibility Rules are worse than Stochastic Ranking.

Over the years, ant colony optimisation (ACO) algorithms have been proposed particularly for solving the hard combinatorial optimisation problems, such as the travelling salesman problem (TSP) and the job-shop scheduling problem (JSSP). Also, most real-world applications are concerned with the multi-objective optimisation problems. In this paper a new ant colony optimisation (ACO) algorithm is proposed for solving two or more objective functions, simultaneously. It is based on the ant colony system (ACS) algorithm and uses the random weight-based method. It is applied on several benchmark instances of the TSP and the JSSP from the literature and compared with more recent multi-objective ant colony optimisation algorithms (MOACO). The experimental results have shown that the proposed algorithm achieves better performance for solving the travelling salesman problem and the job-shop scheduling problem with multiple objectives. It also obtained well distribution all over the Pareto-optimal front.

This paper presents a method of optimized PID parameter self-adapted ant colony algorithm with aberrance gene, based on ant colony algorithm. This method overcomes genetic algorithm’s defects of repeated iteration, slower solving efficiency, ordinary ant colony algorithm’s defects of slow convergence speed, easy to get stagnate, and low ability of full search. For a given system, the results of simulation experiments which compare to the result of Z-N optimization and evolution of genetic algorithm optimization and evolution of ant colony system optimization, it has more excellent performance in finding best solution and convergence, the PID parameters also have optimality, system possesses dynamic controlling and performance. The experiments show that this method has its practical value on controlling other objection and process.

If dedicate a lane to connected autonomous vehicle (CAV) on a multilane road, the traffic congestion and safety risks remain a major problem but in a different style. Random and disorderly mandatory lane‐changing behaviour before approaching the next ramp or intersection would have a disturbing effect on the following vehicles of the traffic flow. This paper mainly establishes the optimal mandatory lane‐changing location matching model for each target vehicle in the dedicated CAV lane environment. The aim is to minimizing the total travel time, which could take the disturbing effect into account. This model nests the cell transmission model (CTM) to describe vehicle running. The constraints include the relation between target CAV lane‐changing cell and the corresponding behaviour start time, the updating of the flow, and occupancy for varied cells. We use the Ant Colony Optimization (ACO) algorithm to solve the problem. Through the case study of a basic two‐lane road scenario in Ningbo, we acquire the convergence results based on the ACO algorithm. Our optimal lane‐changing location matching scheme can save 5.9% total travel time when compared to the near‐end location lane‐changing scheme. We test our model by increasing the total number of upstream input vehicles with 4%, 11%, 15%, and the mandatory lane‐changing vehicles with 60%, 200%, respectively. The testing results prove that out optimization method could deal with varied road traffic flow situations. Specifically, when the traffics and mandatory lane‐changing vehicles increase, our method could perform better.

Hybrid metaheuristic algorithms have become popular for solving complex problems that are very challenging to overcome with conventional methods in recent years. This paper presents a hybrid algorithm called DE-ACO that combines Differential Evolution (DE) and Ant Colony Optimization (ACO) algorithms. The main idea is to use the Differential Evolution (DE) algorithm for a good starting point for Ant Colony Optimization (ACO) algorithm. A benchmark of six well-known test functions is employed to check the performances of the proposed approach in terms of convergence rate, quality of optimum solutions and computing time. The results obtained show that the performances of the hybrid algorithm outperform significantly the DE and ACO algorithms.

Ant Colony Optimization (ACO) is a popular meta-heuristic for solving combinatorial optimization problems. ACO uses the concept of ants foraging for food to find good solutions to these types of problems. ACO has been successfully applied to many problems, from the traveling salesman problem (TSP), to the problem of network routing. However, it has been pointed out that ACO does not perform as well as other heuristics in very dynamic problems. At first, this statement seems strange but a close look reveals that the nature strategy that inspires the ACO meta-heuristic has an important element that is lacking in ACO: evolution. This paper proposes a new algorithm, named Evolutionary Ant Colony Optimization (EACO), that combines ACO with elements of traditional Genetic Algorithms (GA), namely: selection, recombination, and mutation. Individual ants are endowed with a genotype that is allowed to evolve through generations of the population. In doing this, the EACO algorithm adds another element of optimization to the ACO algorithm that allows the individual agents (ants) in the algorithm to improve their behavior over several generations. Our results demonstrate that EACO can indeed overcome the hurdles faced by the original ACO.KeywordsGenetic AlgorithmShort PathTravel Salesman ProblemTravel Salesman ProblemSwarm IntelligenceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper compares ant colony optimization (ACO) and evolutionary multi-objective optimization (EMO) for the weather avoidance in a free flight environment. The problem involves a number of potentially conflicting objectives such as minimizing deviations, weather avoidance, minimizing distance traveled and hard constraints like aircraft performance. Therefore, we modeled the problem as a multi-objective problem with the aim of finding a set of non dominated solutions. This approach is expected to provide pilots the additional degree of freedom necessary for self optimized route planning in Free Flight. Experiments were conducted on a high fidelity air traffic simulator and results indicate that the ACO approach is better suited for this problem, due to its ability to generate solutions in early iterations as well as building better quality non dominated solutions over time.

The ant colony optimization (ACO) algorithm is a meta-heuristic optimization method used to solve challenging optimization problems. Notably, the pheromone model of ACO impacts algorithmic performance. Hence, this paper presents an ACO algorithm with three types of pheromones for solving the component assignment problem of the linear consecutive-k-out-of-n:F system. This configuration can be used to represent a real system in which consecutive failed components cause system failures. Moreover, the component assignment problem seeks a component arrangement in which system reliability is maximized. The proposed algorithm is incorporated with either adjacence-, position-, or k-interval-wise pheromones that are compared using a numerical experiment. The results indicate that the ACO algorithm with the position-wise pheromone performs well within the scope of the experiment.

With the increasing popularity of intelligence, many enterprises’ warehouse inspection work is completed through robots. However, due to the multiple target points of warehouse inspection, the low efficiency of planning intelligent robot inspection paths is a problem that needs to be solved. In order to solve the above problems, this paper proposes an HPSO-ACO algorithm based on hybrid particle swarm optimization (HPSO) to optimize the parameters of the ant colony optimization (ACO) algorithm, and establishes a path optimization model for intelligent inspection robots in warehouse management. Compared with HPSO algorithm and ACO algorithm, the experimental results show that the proposed method has faster convergence speed, fewer iterations, and shorter optimal path under the same conditions, which provides a theoretical reference for path optimization for inspection robot.

In this paper, we propose a zoning plan for reasonable travel time for self driving travelers. There are 201 5A scenic spots in the country. Travelers who want to visit all scenic spots in the face of many time constraints need to optimize travel routes to ensure maximum travel income. Based on this idea, an optimal path analysis based on ant colony algorithm is proposed. After considering time constraints, the time window constraint is transformed into a target function. Constraint, the hard time constraints which must satisfy the limit of driving time limit and the limit of the opening time of the scenic spots are transformed into soft constraints, and the improved ant colony algorithm is used to iterate.With the help of the improved ant colony algorithm, the optimal path selection satisfying the time constraint is obtained. Taking Jiangsu Province as an example, this article draws a tourist route map of Jiangsu province.

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.

In this paper, we propose a hybrid multi-cell tracking approach to accurately and jointly estimate the state and its contour of each cell. Our approach consists of level set evolution and ant colony optimization, representing, respectively, the deterministic and stochastic methods for cell tracking. Firstly, birth ants are directly distributed into the regions depicted by raw curves achieved by the traditional level set evolution. Then, the ants move towards potential regions based on the pheromone deposited by ants and the gradient information of current image. Finally, the resulting pheromone field is embedded in the variational level set to drive the evolution of cell curve to yield an accurate one and correspond cell position estimate. The experiment results show that our method could automatically track multi-cell and achieve an accurate contour estimation of each cell.

The integration of discrete simulations, artificial intelligence methods, and the theory of probability in order to obtain a high flexibility of the production system is crucial. In this paper, the concept of a smart factory operation is proposed along with the idea of data exchange architecture, simulation creation, performance optimization, and predictive analysis of the production process conditions. A Digital Twin for a hybrid flow shop from the automotive industry is presented as a case study. In the paper, the Ant Colony Optimization (ACO) algorithm is developed for multi-criteria scheduling problems in order to obtain a production plan without delays and maximum resource utilization. The ACO is compared to the immune algorithm and genetic algorithm. The best schedules are achieved with low computation time for the Digital Twin. By predicting the reliability parameters of the limited resources of the Digital Twin, stable deadlines for the implementation of production tasks are achieved. Mean Time To Failure and Mean Time of Repair are predicted for a real case study of an electric steering gear production line. The presented integration and data exchange between the elements of the smart factory: a Digital Twin, a computing module including an optimization, prediction, and simulation methods fills the gap between theory and practice for Industry 4.0. The paper presents measurable benefits of integration of discrete simulation tools, historical data analysis, and optimization methods.

Based on the study of the vehicle routing problem and ant colony algorithm, this thesis stablish the guided electric vehicle routing model with battery capacity, cargo loading, soft time window constraints. This model manages to formulate the operation rule using fast, low energy consumption and punctuality as indicators. Based on the mathematical model constructed, the model is solved by ant colony algorithm, and the improved artificial potential field method is used to avoid obstacles, so as to arrange a reasonable driving route for the vehicle. The simulation is performed to prove the feasibility of the model and locate the recharging station. The fuel logistic vehicle model and the guided electric vehicle model are compared and analyzed. Through the calculation, the cost and mileage of the later are close to that of the former; meanwhile the emission of the latter is less than that of the former, indicating the advantages of the guided electric vehicle model.

Due to the emerging of modern evolution of the Fifth Generation (5G) wireless technology and the growth of data traffic to meet various communication requirements of future applications. Routing and path planning in 5G wireless networks is one of the challenges brought about by the variation of network features like traffic load and topology which may differ in stochastic and time varying manner. However, as the mobile users' growth, there is a need to efficient routing strategy for shortest path to forward user requests to the servers to reach services. To meet this aim, an adaptive Meta-heuristic Ant Colony Optimization (ACO) algorithm has proposed in this paper. ACO based on swarm intelligence techniques that has considered inspired algorithm by imitating the sociological attitude related to ants crowd. ACO like the other evolutionary algorithms based on population; it initiates with random solutions of the population. This work was implemented using NS3 simulation tool with different scenarios and parameters. The computational results have shown that the ACO method is better to find the optimal solution in 5G networks than the traditional inelastic way. The simulation outcome indicates the efficiency of the proposed method where the packet delivery ratio increased while the drop packets ratio and the transmission delay reduced.