A genetic algorithm with jumping gene and heuristic operators for traveling salesman problem

A New Approach Based on Collective Intelligence to Solve Traveling Salesman Problems

This paper presents a novel approach based on the ant system algorithm for solving discrete optimization problems. The proposed method is based on path construction, path improvement techniques, and the footprint mechanism. Some information about the optimization problem and collective intelligence is used in order to create solutions in the path construction phase. In the path improvement phase, neighborhood operations are applied to the solution, which is the best of the population and is obtained from the path construction phase. The collective intelligence in the path construction phase is based on a footprint mechanism, and more footprints on the arc improve the selection chance of this arc. A selection probability is also balanced by using information about the problem (e.g., the distance between nodes for a traveling salesman problem). The performance of the proposed method has been investigated on 25 traveling salesman problems and compared with state-of-the-art algorithms. The experimental comparisons show that the proposed method produced comparable results for the problems dealt with in this study.

Emergent multipath COVID-19 specimen collection problem with green corridor through variable length GA

The COVID-19 pandemic has spread worldwide exponentially. Typically, for testing, a provincial main government hospital cum testing center collects patients' specimens from remote health centers in the minimum possible time, satisfying the 'false negativity' constraint of the first collected specimen. With infrastructural developments throughout the world, multiple paths are available for transportation between two cities. Currently, the 'green corridor' is used for the transportation of human organs to be implanted, travel of VIPs, etc., in the minimum possible time. Taking these facts in consideration, for the first time, a green corridor system is suggested to provide a transportation pathway from small hospitals and urban/rural health centers to the testing center with COVID-19 specimens such as blood, nasal and throat swabs, and viral RNA, within the first collected specimen's life period. As health centers are located in different places, appropriate routing plans are needed for visiting them in the minimum possible time. A problem arises if this routing time exceeds the 'false negativity' of the first collected specimen. Thus, multipath COVID-19 specimen collection problems (MPC-19SCPs) are mathematically formulated to be collected from all health centers, and optimum routing plans are obtained using fixed and variable length genetic algorithms (VLGAs) developed for this purpose. For the first time, green corridor systems are suggested to incorporate the centers. The objectives of the models are, subject to the 'false-negative" constraint, minimization of the system time (Model A) and the green corridor time without or with mutual cooperation among the minimum number of centers for the transfer of specimens (Models B and C, respectively). The developed algorithms are based on variable length chromosomes, probabilistic selection, comparison crossover and generation-dependent mutation. Some benchmark instances from TSPLIB are solved by VLGA and GA. The competitiveness of VLGA is established through ANOVA. The models are numerically demonstrated, and some conclusions are derived.