Now showing items 1-6 of 6
An Adaptive Local Search Algorithm for Real-Valued Dynamic Optimization
(IEEE Press, 2015-05)
This paper proposes a novel adaptive local search algorithm for tackling real-valued (or continuous) dynamic optimization problems. The proposed algorithm is a simple single-solution based metaheuristic that perturbs the ...
An adaptive multi-swarm optimizer for dynamic optimization problems
(The MIT Press, 2014-01-17)
The multi-population method has been widely used to solve dynamic optimization problems (DOPs) with the aim of maintaining multiple populations on different peaks to locate and track multiple changing optima simultaneously. ...
Population-based incremental learning with immigrants schemes in changing environments
The population-based incremental learning (PBIL) algorithm is a combination of evolutionary optimization and competitive learning. PBIL has been successfully applied to dynamic optimization problems (DOPs). It is well known ...
Ant colony optimization for dynamic combinatorial optimization problems
(The Institution of Engineering and Technology, 2018-02)
The ant colony optimization (ACO) metaheuristic was inspired from the foraging behaviour of real ant colonies. In particular, real ants communicate indirectly via pheromone trails and find the shortest path. Although real ...
Elitism-based immigrants for ant colony optimization in dynamic environments: Adapting the replacement rate
(IEEE Press, 2014-09-22)
The integration of immigrants schemes with ant colony optimization (ACO) algorithms showed promising results on different dynamic optimization problems (DOPs). The principle of integrating immigrants schemes within ACO is ...
Evolutionary computation for dynamic optimization problems
Many real-world optimization problems are subject to dynamic environments, where changes may occur over time regarding optimization objectives, decision variables, and/or constraint conditions. Such dynamic optimization ...