Multiple solutions are often needed because of different kinds of uncertain failures in a plan execution process and scenarios for which precise mathematical models and constraints are difficult to obtain. This paper proposes an optimization strategy for multirobot task allocation (MRTA) problems and makes efforts on offering multiple solutions with same or similar quality for switching and selection. Since the mentioned problem can be regarded as a multimodal optimization one, this paper presents a niching immune-based optimization algorithm based on Softmax regression (sNIOA) to handle it. A prejudgment of population is done before entering an evaluation process to reduce the evaluation time and to avoid unnecessary computation. Furthermore, a guiding mutation (GM) operator inspired by the base pair in theory of gene mutation is introduced into sNIOA to strengthen its search ability. When a certain gene mutates, the others in the same gene group are more likely to mutate with a higher probability. Experimental results show the improvement of sNIOA on the aspect of accelerating computation speed with comparison to other heuristic algorithms. They also show the effectiveness of the proposed GM operator by comparing sNIOA with and without it. Two MRTA application cases are tested finally.

In multirobot patrolling problems, various dynamic situations require a higher level of cooperation among robots. The dynamic problems caused by moving objects are rarely studied yet. This work proposes a distributed event-driven cooperative strategy for multirobot systems to patrol moving objects autonomously. First, forward and backward utility functions are defined as criteria for robots to conduct two-way evaluation when they choose their targets to patrol. Then, three event types and a cooperative action considering energy consumption and visiting frequency comprehensively are proposed to improve coordination among robots during their execution processes. In simulation experiments, the proposed strategy shows significant advantages on decreasing the average and maximum unvisited time of moving objects compared with the state-of-the-art. A marine pollution monitoring case is simulated to demonstrate the practicability of this strategy.

Multirobot patrolling systems with various sensing and communications devices are deployed to guarantee maritime safety. Patrolling path planning for multiple robots can be modeled as a multiobjective optimization problem. The positions of patrolling nodes impact the length of patrolling paths and execution efficiency of robots. To compute them, a huge solution space is encountered. Besides, multiple patrolling nodes on the same line lead to the same patrolling scheme. Thus, how to promote solution (population) diversity becomes a new challenge. To tackle it, this work proposes a learning-inspired immune algorithm. It uses the historical information in the previous generations during iterations to realize a learning process. Unlike saving all the individuals themselves and training a model for them, the useful historical information is extracted by using upper confidence bound-based and actor–critic-inspired methods. Both time consumption and storage space can be dramatically saved. The experimental results indicate that the proposed algorithm can generate multiple patrolling schemes for the decision makers and outperforms the state-of-the-art.

Public health research and interventions often assume that men who have sex with men (MSM) who use the Internet in China have similar characteristics to those in Western countries, though with little empirical evidence. This study aimed to describe and examine the sociodemographic and basic sexual behavioral characteristics of an online sample of MSM in Guangdong, China. In 2010, a total of 1100 MSM were recruited from an lesbian, gay, bisexual, and transgender (LGBT)-oriented website and were asked about their sociodemographic and sexual behavior characteristics. The majority of the participants (77.9%) self-identified as homosexual, and the mean age was 30.0 years (SD = 6.7). About 80% of the participants had attained a college degree and only 4.8% were unemployed. About 60% had a monthly salary of more than CNY 3000 (476 USD), and more than 10% were married. The majority (71.7%) had used condoms in the last anal sex. Nearly half of the participants have never been tested for HIV or other sexually transmitted infections (STIs) (47.3% and 47.7%, respectively). More than 80% were willing to be contacted by researchers after the survey. Findings indicate that the sociodemographic characteristics of Chinese MSM who use the Internet are relatively similar to those in the Western countries. However, Chinese MSM are less likely to self-identify as homosexual and be tested for HIV and other STIs than Western MSM. On a positive note, Chinese MSM would be likely to engage in e-technology research showing potential feasibility of an online HIV/STI intervention.

Considering the characteristics of Internet-of-Things enabled multirobot maritime patrolling problems which are not reflected in general multiobjective optimization benchmarks, this work proposes a similarity evaluation framework to enhance the diversity when the population evolves. A novel masking thinking is proposed to eliminate the effects of dominant and highly correlated genetic positions and thus magnify the impacts of the rest which can distinguish individuals more effectively. The perspective from objective space is also considered to maintain a desired balance between diversity and convergence. With the use of the proposed similarity evaluation framework, individuals contributing more to the diversity can be selected to the next generation and further evolved. It is beneficial to prevent a population from being trapped by local optima and thus explore better Pareto-optimal solutions. The effectiveness of the proposed framework is validated by ablation experiments. Comparisons to the state-of-the-art are also conducted to illustrate its advantages.