Opposition-based Learning (OBL) is a new concept in machine learning, inspired from the
opposite relationship among entities. In 2005, for the first time the concept of opposition was …

Wireless sensor node coverage optimization is a critical issue in wireless sensor networks
(WSN), which is a commonly typical NP-hard problem. To enhance the coverage of wireless …

Parameters estimation of photovoltaic (PV) model based on experimental data plays an
important role in the simulation, evaluation, control, and optimization of PV systems. In the …

Many optimization problems in reality have become more and more complex, which promote
the research on the improvement of different optimization algorithms. The particle swarm …

Artificial bee colony (ABC) and its most modifications use a probability method to select
good food sources (called solutions) in the onlooker bee search phase. However, the …

This paper explores biogeography-based learning particle swarm optimization (BLPSO).
Specifically, based on migration of biogeography-based optimization (BBO), a new …

Differential evolution (DE) is a robust optimizer designed for solving complex domain
research problems in the computational intelligence community. In the present work, a multi …

Due to the growing demand for clean and sustainable energy sources, there has been an
increasing interest in solar cells and photovoltaic panels. Nevertheless, determining the right …

As a popular swarm intelligence algorithm, artificial bee colony (ABC) achieves excellent
optimization performance, but it has some shortcomings. In order to strengthen the …

DNA computing has efficient computational power, but requires high requirements on the
DNA sequences used for coding, and reliable DNA sequences can effectively improve the …