Gradient-based optimizer (GBO) is a metaphor-free mathematic-based algorithm proposed in recent years. Encouraged by the gradient-based Newton's method, this algorithm combines with population-based evolutionary methods. The disadvantage of the traditional GBO algorithm is that the global search ability of the algorithm is too strong, and the local search ability is too weak; accordingly, it is difficult to obtain the global optimal solution efficiently. Therefore, a new improved GBO algorithm (GOMGBO) is developed to mitigate such performance concerns by introducing a Gaussian bare-bones mechanism, an opposition-based learning mechanism, and a moth spiral mechanism enhanced GBO algorithm. The proposed GOMGBO has been compared against many famous methods and improved variants on 30 benchmark functions. The experimental results show that GOMGBO has apparent advantages in convergence speed and precision. In addition, this paper analyzes the balance and diversity of the GOMGBO algorithm and compares GOMGBO with other algorithms on several engineering problems. The experimental results show that the GOMGBO algorithm is also better than the competitive algorithm in engineering problems. This study uses the GOMGBO algorithm to optimize kernel extreme learning machine (KELM), and a new GOMGBO-KELM model is proposed. The model is used to deal with four clinical disease diagnosis problems. Compared with GBO-KELM, back propagation neural network algorithm, and other models, comparative experiments show that GOMGBO-KELM has high performance in dealing with practical cases. We invite the community to investigate further our method for solving problems more efficiently with reasonable speed and efficiency. Readers of this study can refer to https://aliasgharheidari.com for any guidance about the proposed GOMGBO method.

Data delivery and acquisition are the main factors needed for the success of any proposed Internet of Medical Things (IoMT) systems. To achieve good performance and high quality of services in IoMT systems, data acquisition, and delivery should be performed accurately. In general, IoMT systems are usually vulnerable to the collected data with missing value(s) since missing data is the main problem that affects the overall performance of any system. This leads to a reduction in the satisfaction level of end users. Missing data for IoMT systems originates from a number of sources, including bad connections, outside attack, or sensing errors. To obtain a high performance in such systems, missing data should be imputed once occurred. In this paper, a dynamic adaptive network-based fuzzy inference system (D-ANFIS) approach is proposed to impute the missing values in a simple yet accurate manner. The major contribution is to impute the missing value(s) once received by dividing the collected data into two groups: 1) complete dataset (without missing data) and 2) incomplete dataset (with missing data). A holdout method is used to train the D-ANFIS using complete data, while the incomplete dataset is used to impute the missing value(s). Two methods are used to evaluate the final performance of IoMT application: 1) adaptive network-based fuzzy inference system (ANFIS) with genetic algorithm (ANFIS-GA) and 2) ANFIS with particle swarm optimization (ANFIS-PSO). The results show that the performance of IoMT is enhanced 5% using ANFIS-GA and 3% using ANFIS-PSO.

This paper presents a multi-objective post enrolment course timetabling problem as a new case study. We added a new soft constraint to the original single objective problem to both increase the complexity and represent a real world course timetabling problem. The new soft constraint introduced here attempts to minimize the total number of waiting timeslots in between courses for every student in a day. We proposed a Non-dominated Sorting Genetic Algorithm-II with a variable population size, called NSGA-II VPS, based on a given lifetime for each individual that is evaluated at the time of its birth. The algorithm was tested on the standard benchmark problems and experimental results show that the proposed method demonstrably improved upon the original approach (NSGA-II).

Hyper-heuristics can be defined as search method for selecting or generating heuristics to solve difficult problem. A high level heuristic therefore operate on a set of low level heuristics with the overall aim of selecting the most suitable set of low level heuristics at a particular point in generating an overall solution. In this work, we propose a set of constructive hyper-heuristics for solving attribute reduction problems. At the high level, the hyper-heuristics (at each iteration) adaptively select the most suitable low level heuristics using roulette wheel selection mechanism. Whilst, at the underlying low level, four low level heuristics are used to gradually, and indirectly construct the solution. The proposed hyper-heuristics has been evaluated on a widely used UCI datasets. Results show that our hyper-heuristic produces good quality solutions when compared against other metaheuristic and outperforms other approaches on some benchmark instances.