Activity-based scheduling optimization is a combinatorial problem built on the traveling
salesman problem intending to optimize people schedules considering their trips and the …

An original swarm-based, bio-inspired metaheuristic algorithm, named electric eel foraging
optimization (EEFO) is developed and tested in this work. EEFO draws inspiration from the …

In this paper, a novel and powerful metaheuristic optimizer, named the growth optimizer
(GO), is proposed. Its main design inspiration originates from the learning and reflection …

Recently, research has shown an increased spread of non-communicable diseases such as
cancer. Lung cancer diagnosis and detection has become one of the biggest obstacles in …

The use of metaheuristics is one of the most encouraging methodologies for taking care of
real-life problems. Bald eagle search (BES) algorithm is the latest swarm-intelligence …

Feature selection is an NP-hard problem to remove irrelevant and redundant features with
no predictive information to increase the performance of machine learning algorithms. Many …

Optimization of multi-modal functions is challenging even for evolutionary and swarm-based
algorithms as it requires an efficient exploration for finding the promising region of the …

The African vultures optimization algorithm (AVOA) is a recently proposed metaheuristic
inspired by the African vultures' behaviors. Though the basic AVOA performs very well for …

To address the problem of the sparrow search algorithm (SSA) has poor global search
ability, weak local development ability, and easily falls into the local optimal solution, a multi …

This paper proposes a new nature-inspired metaheuristic algorithm called Clouded Leopard
Optimization (CLO), which mimics the natural behavior of clouded leopards in the wild. The …