Abstract Central Force Optimization (CFO) is a new deterministic multi-dimensional search
metaheuristic based on the metaphor of gravitational kinematics. It models" probes" that" fly" …

Large deformation near surface excavations and openings has been frequently simulated in
geotechnical problems. Mesh-based approaches for large-scale simulation have long been …

Evolutionary algorithms have gained significant attention from researchers as effective
solutions for various optimization problems. Genetic Algorithm (GA) is widely popular due to …

Designing a controller is typically an iterative process during which engineers must assess
the performance of a design through time-consuming simulations; this becomes even more …

The goal of this paper is to show how swarm intelligence inspired optimization algorithms
can take benefit of the parallel computing mechanism supported by general purpose …

This paper introduces an innovative approach to boost the efficiency and scalability of
Evolutionary Rule-based machine Learning (ERL), a key technique in explainable AI. While …

Based on a literature review conducted of the numerous solutions available, various gaps
were identified in the context of AR guided assembly and formed into three research issues …

Effectiveness analysis on population-based meta-heuristics can be a longstanding task,
specifically when dealing with large populations. This makes this class of algorithms …

Design and optimization (D&O) problems in applied electromagnetics, in particular antenna
D&O, often rely on global search and optimization metaheuristics based on Nature-inspired …

Abstract Central Force Optimization is a relatively new (2007) optimization technique based
on Newtonian equations for objects moving through friction-less space. Unlike Particle …