The Optimal Scheduling of a Reversing Strip Mill: Studies Using Multipopulation Genetic Algorithms and Differential Evolution

2003 ◽  
Vol 18 (3) ◽  
pp. 433-445 ◽  
Author(s):  
N. Chakraborti ◽  
A. Kumar
Keyword(s):  
Genetic Algorithms ◽  
Differential Evolution ◽  
Optimal Scheduling

scholarly journals A Novel Parallel SPEA2 for Solving the Environmental-Economic Dispatch Problem: Competitive vs Cooperative Approach

2020 ◽  
Author(s):  
João Pedro Augusto Costa ◽  
Omar Andres Carmona Cortes ◽  
Osvaldo Ronald Saavedra
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Environmental Economic ◽  
Differential Evolution ◽  
Economic Dispatch ◽  
Pareto Frontier ◽  
Parallel Model ◽  
Swarm Optimization ◽  
Cooperative Approach ◽  
Economic Dispatch Problem

This paper aims to compare two different parallel approaches (cooperative and competitive) of the SPEA2 for solving the environmental-economic dispatch problem. The idea is to solve the problem by executing the SPEA2 algorithm along with three different meta-heuristics (Genetic Algorithms, Particle Swarm Optimization, and Differential Evolution) to perform changes in the population. The different meta-heuristics work in parallel using two different approaches. The first one is the competitive approach, in which meta-heuristics compete for producing the best set of candidate solutions for solving the problem. Whereas, the cooperative approach selects the new population merging all individuals from all meta-heuristics, then selecting the solution set for the Pareto frontier. The proposal was implemented in C++ using MPI in a master-slave parallel model. Two  study cases were used: the first one with six generators and the second one with forty generators. Results showed that the cooperative approach presented the best Pareto frontier for the case of 40 generators.

scholarly journals Autonomous Planning of Multigravity-Assist Trajectories with Deep Space Maneuvers Using a Differential Evolution Approach

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ossama Abdelkhalik
Keyword(s):  
Genetic Algorithms ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Optimal Number ◽  
Deep Space ◽  
Biologically Inspired ◽  
Autonomous Planning ◽  
Design Variables ◽  
Planning Problems ◽  
Evolution Approach

The biologically inspired concept of hidden genes has been recently introduced in genetic algorithms to solve optimization problems where the number of design variables is variable. In multigravity-assist trajectories, the hidden genes genetic algorithms demonstrated success in searching for the optimal number of swing-bys and the optimal number of deep space maneuvers. Previous investigations in the literature for multigravity-assist trajectory planning problems show that the standard differential evolution is more effective than the standard genetic algorithms. This paper extends the concept of hidden genes to differential evolution. The hidden genes differential evolution is implemented in optimizing multigravity-assist space trajectories. Case studies are conducted, and comparisons to the hidden genes genetic algorithms are presented in this paper.

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