A Hybrid Cellular Genetic Algorithm for the Traveling Salesman Problem

The traveling salesman problem (TSP), a typical non-deterministic polynomial (NP) hard problem, has been used in many engineering applications. Genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. However, it has some issues for solving TSP, including quickly falling into the local optimum and an insufficient optimization precision. To address TSP effectively, this paper proposes a hybrid Cellular Genetic Algorithm with Simulated Annealing (SA) Algorithm (SCGA). Firstly, SCGA is an improved Genetic Algorithm (GA) based on the Cellular Automata (CA). The selection operation in SCGA is performed according to the state of the cell. Secondly, SCGA, combined with SA, introduces an elitist strategy to improve the speed of the convergence. Finally, the proposed algorithm is tested against 13 standard benchmark instances from the TSPLIB to confirm the performance of the three cellular automata rules. The experimental results show that, in most instances, the results obtained by SCGA using rule 2 are better and more stable than the results of using rule 1 and rule 3. At the same time, we compared the experimental results with GA, SA, and Cellular Genetic Algorithm (CGA) to verify the performance of SCGA. The comparison results show that the distance obtained by the proposed algorithm is shortened by a mean of 7% compared with the other three algorithms, which is closer to the theoretical optimal value and has good robustness.

Multi-Attributes Decision-Making for CDO Trajectory Planning in a Novel Terminal Airspace

Continuous Descent Operations (CDO) has been recognized as an effective way to significantly reduce fuel burn and noise impact. Designing efficient and flexible arrival routes for generating conflict-free and economical trajectories is a cornerstone for fully achieving CDO by high-level automation in high-density traffic scenarios. In this research, inspired by the Point Merge (PM), we design the Inverted Crown-Shaped Arrival Airspace (ICSAA) and its operational procedures to support Omni-directional CDO. In order to generate optimal conflict-free trajectories for upcoming aircraft in an efficient manner, we established a multi-objective trajectory optimization model solved by Non-dominated Sorting Genetic Algorithm with Elitist Strategy (NSGA-II). The Pareto solutions of minimal fuel consumption and trip time were achieved in single aircraft and highly complex multi-aircraft scenarios. Among all the elements of Pareto front, we obtained an unique solution with Entropy-Weights Method and TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) to strike a better trade-off among collision probability, fuel consumption, and trip time, which incorporates both air traffic controller’s and pilot’s interests. The effectiveness of CDO performance improvement and computational efficiency in different scenarios were verified. The ICSAA would be a promising structure that promotes the application of automated and flexible CDO.

Layout optimization of a military operations center using a genetic algorithm

A genetic algorithm was developed to support the spatial layout design of military operations centers. Based on an abstract representation of the workplace, the algorithm uses a textual string as the genetic encoding method, two genetic operations (i.e., selection and swap) for simulating an evolution process, a fitness function that reflects a human factors characterization of workplace layout requirements, and an elitist strategy for improving its search efficiency. The effectiveness of the algorithm was demonstrated in the design of a mid-sized operations center that involved a team of 68 operators. This algorithm expands the human factors practitioners’ toolkit and enhances their ability to examine layout options of complex workplaces using modeling and simulation.

Multi-objective parameters optimization design of self-excited oscillation pulsed atomizing nozzle

The self-excited oscillation pulsed atomizing nozzle can effectively and evenly spray the high-speed solid cone jet without any extra power. The primary atomization of the jet at the outlet of nozzle directly affects the final spray quality, and the turbulence and cavitation at the outlet of the atomizing nozzle are the other two main factors affecting the atomization. In this work, multi-objective optimization depending on nozzle parameters was established by using mathematical optimization techniques and computational fluid dynamics to improve the jet atomization quality at the outlet of the nozzle. The central composite design method and the response surface method were used to obtain the approximate mathematical model of the primary atomization quality of the jet at the outlet of nozzle. Finally, the non-dominated sorting genetic algorithm with elitist strategy (NSGA-II) and the grey theory were used in combination to optimize the nozzle parameters. Through combining with the NSGA-II and the grey theory, the nozzle parameters were optimized in order to obtain the best primary atomization at the outlet area of nozzle. The optimization results verified the nozzle design with multi-objective optimization method. The optimized values of the turbulent kinetic energy F1 and the vapor volume fraction F2 increased by 28.26% and 5.56%, respectively, and the corresponding nozzle parameters of the chamber diameter D, the lower nozzle diameter d2 and the upper nozzle inlet pressure Pin were, respectively, optimized to 28.056 mm, 5.472 mm and 3.999 Mpa.

Hybrid chaos-based particle swarm optimization-ant colony optimization algorithm with asynchronous pheromone updating strategy for path planning of landfill inspection robots

Robots are coming to help us in different harsh environments such as deep sea or coal mine. Waste landfill is the place like these with casualty risk, gas poisoning, and explosion hazards. It is reasonable to use robots to fulfill tasks like burying operation, transportation, and inspection. In these assignments, one important issue is to obtain appropriate paths for robots especially in some complex applications. In this context, a novel hybrid swarm intelligence algorithm, ant colony optimization enhanced by chaos-based particle swarm optimization, is proposed in this article to deal with the path planning problem for landfill inspection robots in Asahikawa, Japan. In chaos-based particle swarm optimization, Chebyshev chaotic sequence is used to generate the random factors for particle swarm optimization updating formula so as to effectively adjust particle swarm optimization parameters. This improved model is applied to optimize and determine the hyper parameters for ant colony optimization. In addition, an improved pheromone updating strategy which combines the global asynchronous feature and “Elitist Strategy” is employed in ant colony optimization in order to use global information more appropriately. Therefore, the iteration number of ant colony optimization invoked by chaos-based particle swarm optimization can be reduced reasonably so as to decrease the search time effectively. Comparative simulation experiments show that the chaos-based particle swarm optimization-ant colony optimization has a rapid search speed and can obtain solutions with similar qualities.

Improved Monarch Butterfly Optimization Algorithm Based on Opposition-Based Learning and Random Local Perturbation

Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.