A multi-objective multi-agent optimization algorithm for the multi-skill resource-constrained project scheduling problem with transfer times

This paper addresses the Multi-Skill Resource-Constrained Project Scheduling Problem with Transfer Times (MSRCPSP-TT). A new model has been developed that incorporates the presence of transfer times within the multi-skill RCPSP. The proposed model aims to minimize project’s duration and cost, concurrently. The MSRCPSP-TT is an NP-hard problem; therefore, a Multi-Objective Multi-Agent Optimization Algorithm (MOMAOA) is proposed to acquire feasible schedules. In the proposed algorithm, each agent represents a feasible solution that works with other agents in a grouped environment. The agents evolve due to their social, autonomous, and self-learning behaviors. Moreover, the adjustment of environment helps the evolution of agents as well. Since the MSRCPSP-TT is a multi-objective optimization problem, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used in different procedures of the MOMAOA. Another novelty of this paper is the application of TOPSIS in different procedures of the MOMAOA. These procedures are utilized for: (1) detecting the leader agent in each group, (2) detecting the global best leader agent, and (3) the global social behavior of the MOMAOA. The performance of the MOMAOA has been analyzed by solving several benchmark problems. The results of the MOMAOA have been validated through comparisons with three other meta-heuristics. The parameters of algorithms are determined by the Response Surface Methodology (RSM). The Kruskal–Wallis test is implemented to statistically analyze the efficiency of methods. Computational results reveal that the MOMAOA can beat the other three methods according to several testing metrics. Furthermore, the impact of transfer times on project’s duration and cost has been assessed. The investigations indicate that resource transfer times have significant impact on both objectives of the proposed model

A New Meta-heuristic Algorithm based on Multi-criteria Decision Making to Solve Community Detection Problem

Community detection is one of the most significant issues in the field of social networks. The main purpose of community detection is to partition the network in such a way that the relations between components of the network are dense. Because of the strong relations among network members in these partitions, you can consider them as a community. Many researchers have developed several algorithms to solve such a problem. Therefore, we present a genetic algorithm based on Topsis which is a multi-criteria decision making method (MCDM). The proposed algorithm uses Topsis to rank solutions based on modularity and modularity density which are two of the most well-known criteria in community detection problem. Thereafter, crossover and mutation operators are only applied on solutions ranked by Topsis. The performance of the proposed algorithm has been evaluated through comparing it against classical genetic algorithm and a greedy one. The results showed that the proposed algorithm outperforms the other two methods. Since the application of MCDM approach has not been reported in the related literature, this paper can be considered as a basis for future studies

A Hybrid Algorithm for Detecting Communities of Social Networks based on the Modularity Density Criterion

Detecting existing communities in social networks is a significant process in analyzing these networks. In recent years, the community detection problem has become popular for detecting structures of social networks. Due to high importance of this problem, various algorithms have been developed in the literature to find communities of complex networks. In this research, a hybrid meta-heuristic consisting of the genetic algorithm (GA) and the invasive weed optimization (IWO) method have been proposed which aims to find appropriate and high quality solutions for the community detection problem. In this hybrid method, the initial solutions are generated via the IWO algorithm, and thereafter the optimization process is continued by means of the genetic algorithm. The proposed algorithm is known as the GAIWO. Fitness of solutions is determined in terms of the modularity density criterion. Modularity density has a maximization essence and determines the quality of detected communities. To evaluate the efficiency of the GAIWO, four other methods have been employed and their results have been compared. Comparisons have been made on several networks with different sizes. Input parameters of all algorithms have been tuned by a design of experiments approach. The outputs indicate appropriate efficiency of the proposed algorithm. Validation of the results have been investigated by means of the Normalized Mutual Information (NMI) metric

Global prevalence of drug-resistant tuberculosis: a systematic review and meta-analysis

Abstract Background Tuberculosis is a bacterial infectious disease, which affects different parts of a human body, mainly lungs and can lead to the patient’s death. The aim of this study is to investigate the global prevalence of drug-resistant tuberculosis using a systematic review and meta-analysis. Methods In this study, the PubMed, Scopus, Web of Science, Embase, ScienceDirect and Google Scholar repositories were systematically searched to find studies reporting the global prevalence of drug-resistant tuberculosis. The search did not entail a lower time limit, and articles published up until August 2022 were considered. Random effects model was used to perform the analysis. The heterogeneity of the studies was examined with the I 2 test. Data analysis was conducted within the Comprehensive Meta-Analysis software. Results In the review of 148 studies with a sample size of 318,430 people, the I 2 index showed high heterogeneity (I 2  = 99.6), and accordingly random effects method was used to analyze the results. Publication bias was also examined using the Begg and Mazumdar correlation test which indicated the existence of publication bias in the studies (P = 0.008). According to our meta-analysis, the global pooled prevalence of multi-drug resistant TB is 11.6% (95% CI: 9.1–14.5%). Conclusions The global prevalence of drug-resistant tuberculosis was found to be very high, thus health authorities should consider ways to control and manage the disease to prevent a wider spread of tuberculosis and potentially subsequent deaths