Now showing items 11-20 of 207
Convergence versus diversity in multiobjective optimization
Convergence and diversity are two main goals in multiobjective optimization. In literature, most existing multiobjective optimization evolutionary algorithms (MOEAs) adopt a convergence-first-and-diversity-second environmental ...
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 ...
A steady-state and generational evolutionary algorithm for dynamic multi-objective optimization
(IEEE Press, 2016-05-10)
This paper presents a new algorithm, called steady-state and generational evolutionary algorithm, which combines the fast and steadily tracking ability of steady-state algorithms and good diversity preservation of generational ...
Empirical study on the effect of population size on MAX-MIN ant system in dynamic environments
(IEEE Press, 2016-07)
In this paper, the effect of the population size on the performance of the MAX -MIN ant system for dynamic optimization problems (DOPs) is investigated. DOPs are generated with the dynamic benchmark generator for ...
Benchmark Generator for the IEEE WCCI-2014 Competition on Evolutionary Computation for Dynamic Optimization Problems: Dynamic Rotation Peak Benchmark Generator (DRPBG) and Dynamic Composition Benchmark Generator (DCBG)
(De Montfort University, UK, 2013-10)
Based on our previous benchmark generator for the IEEE CEC’12 Competition on Dynamic Optimization, this report updates the two benchmark instances where two new features have 1been developed as well as a constraint to the ...
Evolutionary dynamic optimization: test and evaluation environments.
A survey of swarm intelligence for dynamic optimization: Algorithms and applications
Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have ...
A Fast Strength Pareto Evolutionary Algorithm Incorporating Predefined Preference Information
(IEEE Press, 2015-09)
Strength Pareto Evolutionary Algorithm 2 (SPEA2) has achieved great success for handling multiobjective optimization problems. However, it has been widely reported that SPEA2 gets subjected to a huge amount of computational ...
Analyzing evolutionary algorithms for dynamic optimization problems based on the dynamical system.