Initialisation Approaches for Population-Based Metaheuristic Algorithms: A Comprehensive Review

A situation where the set of initial solutions lies near the position of the true optimality (most favourable or desirable solution) by chance can increase the probability of finding the true optimality and significantly reduce the search efforts. In optimisation problems, the location of the global optimum solution is unknown a priori, and initialisation is a stochastic process. In addition, the population size is equally important; if there are problems with high dimensions, a small population size may lie sparsely in unpromising regions, and may return suboptimal solutions with bias. In addition, the different distributions used as position vectors for the initial population may have different sampling emphasis; hence, different degrees of diversity. The initialisation control parameters of population-based metaheuristic algorithms play a significant role in improving the performance of the algorithms. Researchers have identified this significance, and they have put much effort into finding various distribution schemes that will enhance the diversity of the initial populations of the algorithms, and obtain the correct balance of the population size and number of iterations which will guarantee optimal solutions for a given problem set. Despite the affirmation of the role initialisation plays, to our knowledge few studies or surveys have been conducted on this subject area. Therefore, this paper presents a comprehensive survey of different initialisation schemes to improve the quality of solutions obtained by most metaheuristic optimisers for a given problem set. Popular schemes used to improve the diversity of the population can be categorised into random numbers, quasirandom sequences, chaos theory, probability distributions, hybrids of other heuristic or metaheuristic algorithms, Lévy, and others. We discuss the different levels of success of these schemes and identify their limitations. Similarly, we identify gaps and present useful insights for future research directions. Finally, we present a comparison of the effect of population size, the maximum number of iterations, and ten (10) different initialisation methods on the performance of three (3) population-based metaheuristic optimizers: bat algorithm (BA), Grey Wolf Optimizer (GWO), and butterfly optimization algorithm (BOA)

An Empirical Evaluation of the Role of Information and Communication Technology in Advancement of Teaching and Learning

This work reports an investigation into the role of Information and Communication Technology in promoting efficiency in teaching, using Federal University Lafia as a case study. The University is amongst the 9 newly created federal universities in Nigeria. Research questions and hypothesis were developed and used as a guide in the study. Data was collected via a questionnaire. The collated data were analysed using mean and standard deviation, while T-test was used in testing the hypothesis proposed for the study. The results from the sample survey of fifty (50) lecturers show that Information and Communication Technology plays a vital role in promoting efficiency in the teaching process. The T-test analysis show no significant difference between the opinions of Male and Female lecturers for most items that were considered in the course of the stud

A novel binary greater cane rat algorithm for feature selection

There is a surge in the application of population-based metaheuristic algorithms to find the optimal feature subset from high dimensional datasets. Many of these approaches cannot properly scale especially as they are expected to maintain two opposing goals: maximizing the accuracy of classification while at the same time minimizing the number of feature subsets selected. In this study, a novel binary greater cane rat algorithm (GCRA), inspired by intelligent nocturnal behavior of the GCR which significantly affects their foraging and mating activities. They leave trails to food sources, shelters, and water as they forage, and this information is kept by the dominant. Also, they split into male and female groups during mating season is during abundant food supply and near water source. This information is modeled into and effective method for selecting the optimal feature subset from high-dimensional datasets using two different approaches. Firstly, five variants of binary GCRA are developed using one each from the family of S-shaped, V-shaped, U-shaped, Z-shaped, and quadratic transfer functions to binarize the GCRA. Secondly, the threshold which maps a variable to 0 or 1 is used to develop a variant of GCRA. The performance of the six (6) variants were evaluated using 12 datasets with different dimensionalities. The experimental results show the stability of all the proposed methods as they generally performed competitively. However, the threshold version known as BGCRA showed better performance in yielding the highest accuracy of classification on 9 of the 12 datasets utilized in the study and performed second in selecting the least number of important feature sets. It also showed superiority over other variants in yielding the least average fitness values in 11 of 12 (91.6%) of the datasets used. Hence, the BGCRA was utilized for further comparative analysis against 5 other popular feature selection (FS) algorithms with outstanding performance in terms of producing the highest mean accuracy of classification on 91.6% (11 of 12) of the datasets, 100% least average fitness values, and 91.6% in selecting the least average number of significant features. The results were also validated by statistical tests which showed that the BGCRA is significantly superior compared to other methods

Precision of BDMO and other approaches.

Precision of BDMO and other approaches.</p

Standard deviation of fitness values.

Standard deviation of fitness values.</p

Average feature selected by BDMO and other approaches.

Average feature selected by BDMO and other approaches.</p

Mean fitness values.

Mean fitness values.</p

Comparison between the proposed BDMO and the state-of-the-art methods based on accuracy validation on all selected high-dimensional feature selection datasets.

Comparison between the proposed BDMO and the state-of-the-art methods based on accuracy validation on all selected high-dimensional feature selection datasets.</p

Illustration of the convergence curves for the three most prominent approaches employed in this study, namely BDMO, BPSO, and SBWOA to solve all the selected high-dimensional feature selection datasets.

Illustration of the convergence curves for the three most prominent approaches employed in this study, namely BDMO, BPSO, and SBWOA to solve all the selected high-dimensional feature selection datasets.</p

Experiment’s parameter setting.

Experiment’s parameter setting.</p