Now showing items 1-10 of 16
A memetic particle swarm optimization algorithm for multimodal optimization problems.
(Elsevier B.V., 2012)
Recently, multimodal optimization problems (MMOPs) have gained a lot of attention from the evolutionary algorithm (EA) community since many real-world applications are MMOPs and may require EAs to present multiple optimal ...
Ant colony optimization with memory-based immigrants for the dynamic vehicle routing problem.
A recent integration showed that ant colony optimization (ACO) algorithms with immigrants schemes perform well on different variations of the dynamic travelling salesman problem. In this paper, we address ACO for the dynamic ...
Force-imitated particle swarm optimization using the near-neighbor effect for locating multiple optima.
Multimodal optimization problems pose a great challenge of locating multiple optima simultaneously in the search space to the particle swarm optimization (PSO) community. In this paper, the motion principle of particles ...
Ant colony optimization with immigrants schemes for the dynamic vehicle routing problem.
Ant colony optimization (ACO) algorithms have proved to be able to adapt to dynamic optimization problems (DOPs) when they are enhanced to maintain diversity and transfer knowledge. Several approaches have been integrated ...
A memetic particle swarm optimisation algorithm for dynamic multi-modal optimisation problems.
(Taylor & Francis, 2012)
Many real-world optimisation problems are both dynamic and multi-modal, which require an optimisation algorithm not only to find as many optima under a specific environment as possible, but also to track their moving ...
Hyper-mutation based genetic algorithms for dynamic multicast routing problem in mobile ad hoc networks.
In this paper, the problem of dynamic multicast routing in mobile ad hoc networks is investigated. Lots of interesting works have been done on multicast routing since it is proved to be a NP-hard problem. However, most of ...
A benchmark generator for dynamic permutation-encoded problems.
Several general benchmark generators (BGs) are available for the dynamic continuous optimization domain, in which generators use functions with adjustable parameters to simulate shifting landscapes. In the combinatorial ...
Maintaining diversity by clustering in dynamic environments.
Maintaining population diversity is a crucial issue for the performance of evolutionary algorithms (EAs) in dynamic environments. In the literature of EAs for dynamic optimization problems (DOPs), many studies have been ...
Dynamic optimization using analytic and evolutionary approaches: a comparative review.
A hybrid evolutionary multiobjective optimization strategy for the dynamic power supply problem in magnesia grain manufacturing.
(World Federation on Soft Computing (WFSC), 2012)