Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.

BackgroundTo assess the Knowledge, Attitudes and Practice (KAP) amongst the general community regarding type 2 diabetes mellitus (DM) in rural Bangladesh.MethodsData was collected using cluster random sampling from 3104 adults residing in a rural district in Bangladesh. Participants underwent a KAP questionnaire survey regarding assessing diabetes, socio-demographic and medical history. Descriptive, Chi-square and regression analyses were performed.ResultsParticipants were aged between 30 and 89 years (M  = 51, SD  = 11.8) and 65.5% were female. The prevalence of diabetes was found to be 8.3%. The majority (93%) reported to have heard of diabetes, yet only 4% knew what a glucose tolerance test was. Only 50% reported that they knew physical inactivity was a risk factor. Age, gender, level of education and socio-economic status (SES) were significantly associated with KAP. A lower proportion (41%) of older participants (aged ≥65 years) reported that they knew that dietary modifications assist in diabetes control compared to those aged less than 35 years (69%), p<0.001. Males (β  = 0.393, 95% CI = 0.142–0.643), and any level of education compared to no schooling (β  = 0.726, 95% CI = 0.596, 0.857) reported significantly more knowledge, after multivariate adjustments for covariates. Participants aged under 35 years, (odds ratio (OR)  = 1.73, 95% CI  = 1.22–2.43) had significantly higher positive attitudes towards treatments of diabetes compared to those aged ≥65 years. Of the 99 people with known diabetes, more than 50% (n = 52) never had their blood sugar levels checked since diagnosis.ConclusionsKnowledge of diabetes and its risk factors is very limited in rural Bangladesh, even in persons diagnosed with type 2 DM. The development of public health programmes to increase knowledge of diabetes and its complications is required to assist people living in rural Bangladesh to control and management of diabetes.

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The involvement of Meta-heuristic algorithms in robot motion planning has attracted the attention of researchers in the robotics community due to the simplicity of the approaches and their effectiveness in the coordination of the agents. This study explores the implementation of many meta-heuristic algorithms, e.g. Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) in multiple motion planning scenarios. The study provides comparison between multiple meta-heuristic approaches against a set of well-known conventional motion planning and navigation techniques such as Dijkstra's Algorithm (DA), Probabilistic Road Map (PRM), Rapidly Random Tree (RRT) and Potential Field (PF). Two experimental environments with difficult to manipulate layouts are used to examine the feasibility of the methods listed. several performance measures such as total travel time, number of collisions, travel distances, energy consumption and displacement errors are considered for assessing feasibility of the motion planning algorithms considered in the study. The results show the competitiveness of meta-heuristic approaches against conventional methods. Dijkstra 's Algorithm (DA) is considered a benchmark solution and Constricted Particle Swarm Optimization (CPSO) is found performing better than other meta-heuristic approaches in unknown environments.

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