Now showing items 41-50 of 121
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 ...
Pheromone modification strategy for the dynamic travelling salesman problem with weight changes
Ant colony optimization (ACO) algorithms have proved to be able to adapt in problems that change dynamically. One of the key issues for ACO when a change occurs is that the pheromone trails generated in the previous ...
A decomposition-based multiobjective evolutionary algorithm with angle-based adaptive penalty
A multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem (MOP) into a number of scalar optimization subproblems and optimizes them in a collaborative manner. ...
Guest editorial: Memetic computing in the presence of uncertainties
Adaptive neighborhood selection for many-objective optimization problems
It is generally accepted that conflicts between convergence and distribution deteriorate with an increase in the number of objectives. Furthermore, Pareto dominance loses its effectiveness in many-objectives optimization ...
Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays
Train rescheduling after a perturbation is a challenging task and is an important concern of the railway industry as delayed trains can lead to large fines, disgruntled customers and loss of revenue. Sometimes not just one ...
Ant colony optimization with self-adaptive evaporation rate in dynamic environments
(IEEE Press, 2014-12)
The performance of ant colony optimization (ACO) algorithms in tackling optimization problems strongly depends on different parameters. One of the most important parameters in ACO algorithms when addressing dynamic ...
A strength pareto evolutionary algorithm based on reference direction for multi-objective and many-objective optimization
(IEEE Press, 2017-03-24)
While Pareto-based multi-objective optimization algorithms continue to show effectiveness for a wide range of practical problems that involve mostly two or three objectives, their limited application for many-objective ...
Improving the JADE algorithm by clustering successful parameters
(Inderscience Publishers, 2016-09)
Differential evolution (DE) is one of the most powerful and popular evolutionary algorithms for real parameter global optimisation problems. However, the performance of DE greatly depends on the selection of control ...
Benchmark Functions for the CEC'2017 Competition on Many-Objective Optimization
(University of Birmingham, U.K., 2017-01)
In the real world, it is not uncommon to face an optimization problem with more than three objectives. Such problems, called many-objective optimization problems (MaOPs), pose great challenges to the area of evolutionary ...