An enhanced Moth-flame optimization algorithm for permutation-based problems

Enhanced multi-layer perceptron for CO2 emission prediction with worst moth disrupted moth fly optimization (WMFO)

This study introduces the Worst Moth Disruption Strategy (WMFO) to enhance the Moth Fly Optimization (MFO) algorithm, specifically addressing challenges related to population stagnation and low diversity. The WMFO aims to prevent local trapping of moths, fostering improved global search capabilities. Demonstrating a remarkable efficiency of 66.6 %, WMFO outperforms the MFO on CEC15 benchmark test functions. The Friedman and Wilcoxon tests further confirm WMFO's superiority over state-of-the-art algorithms. Introducing a hybrid model, WMFO-MLP, combining WMFO with a Multi-Layer Perceptron (MLP), facilitates effective parameter tuning for carbon emission prediction, achieving an outstanding total accuracy of 97.8 %. Comparative analysis indicates that the MLP-WMFO model surpasses alternative techniques in precision, reliability, and efficiency. Feature importance analysis reveals that variables such as Oil Efficiency and Economic Growth significantly impact MLP-WMFO's predictive power, contributing up to 40 %. Additionally, Gas Efficiency, Renewable Energy, Financial Risk, and Political Risk explain 26.5 %, 13.6 %, 8 %, and 6.5 %, respectively. Finally, WMFO-MLP performance offers advancements in optimization and predictive modeling with practical applications in carbon emission prediction.

Airborne Hyperspectral Imagery for Band Selection Using Moth–Flame Metaheuristic Optimization

In this research, we study a new metaheuristic algorithm called Moth–Flame Optimization (MFO) for hyperspectral band selection. With the hundreds of highly correlated narrow spectral bands, the number of training samples required to train a statistical classifier is high. Thus, the problem is to select a subset of bands without compromising the classification accuracy. One of the ways to solve this problem is to model an objective function that measures class separability and utilize it to arrive at a subset of bands. In this research, we studied MFO to select optimal spectral bands for classification. MFO is inspired by the behavior of moths with respect to flames, which is the navigation method of moths in nature called transverse orientation. In MFO, a moth navigates the search space through a process called transverse orientation by keeping a constant angle with the Moon, which is a compelling strategy for traveling long distances in a straight line, considering that the Moon’s distance from the moth is considerably long. Our research tested MFO on three benchmark hyperspectral datasets—Indian Pines, University of Pavia, and Salinas. MFO produced an Overall Accuracy (OA) of 88.98%, 94.85%, and 97.17%, respectively, on the three datasets. Our experimental results indicate that MFO produces better OA and Kappa when compared to state-of-the-art band selection algorithms such as particle swarm optimization, grey wolf, cuckoo search, and genetic algorithms. The analysis results prove that the proposed approach effectively addresses the spectral band selection problem and provides a high classification accuracy.

Recent Advances and Applications of Spiral Dynamics Optimization Algorithm: A Review

This paper comprehensively reviews the spiral dynamics optimization (SDO) algorithm and investigates its characteristics. SDO algorithm is one of the most straightforward physics-based optimization algorithms and is successfully applied in various broad fields. This paper describes the recent advances of the SDO algorithm, including its adaptive, improved, and hybrid approaches. The growth of the SDO algorithm and its application in various areas, theoretical analysis, and comparison with its preceding and other algorithms are also described in detail. A detailed description of different spiral paths, their characteristics, and the application of these spiral approaches in developing and improving other optimization algorithms are comprehensively presented. The review concludes the current works on the SDO algorithm, highlighting its shortcomings and suggesting possible future research perspectives.