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

Precision of BDMO and other approaches.

Precision of BDMO and other approaches.</p

Mean fitness values.

Mean fitness values.</p

Average feature selected by BDMO and other approaches.

Average feature selected by BDMO and other approaches.</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

The optimization procedures of DMO [15].

The optimization procedures of DMO [15].</p

Results of Wilcoxon sign test on validation accuracy.

Results of Wilcoxon sign test on validation accuracy.</p