The main objective of this paper is to further improve the current time-series prediction
(forecasting) algorithms based on hybrids between machine learning and nature-inspired …

In this paper, an effective improved co-evolution ant colony optimisation (MSICEAO)
algorithm is presented to solve complex optimisation problem. In the MSICEAO, the multi …

To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and
slow global convergence speed in ant colony optimization (ACO) algorithm in solving …

Multilevel image thresholding is a very important image processing technique that is used as
a basis for image segmentation and further higher level processing. However, the required …

Portfolio selection (optimization) problem is a very important and widely researched problem
in the areas of finance and economy. Literature review shows that many methods and …

Artificial bee colony (ABC) algorithm developed by Karaboga is a nature inspired
metaheuristic based on honey bee foraging behavior. It was successfully applied to …

Because of the complicated underwater environment, the efficiency of data transmission
from underwater sensor nodes to a sink node (SN) is faced with great challenges. Aiming at …

Portfolio optimization (selection) problem is an important and hard optimization problem that,
with the addition of necessary realistic constraints, becomes computationally intractable …

Artificial bee colony (ABC) is a relatively new swarm intelligence based metaheuristic. It was
successfully applied to unconstrained optimization problems and later it was adjusted for …

Seeker optimization algorithm is one of the recent swarm intelligence metaheuristics for hard
optimization problems. It is based on the human group search behavior and it was …