The Impact of Representation for Taxonomical Evaluation of Evolutionary Algorithms

2009 ◽  
pp. 213-219
Keyword(s):  
Evolutionary Algorithms ◽  
The Impact

Comparative Study of Evolutionary Algorithms for Parameter Identification of an Impact Oscillator

2014 ◽  
Author(s):  
Amit Banerjee ◽  
Issam Abu Mahfouz
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Parameter Identification ◽  
Optimization Techniques ◽  
System Response ◽  
Impact Oscillator ◽  
Swarm Optimization ◽  
System Parameters ◽  
Highly Nonlinear ◽  
The Impact

The use of non-classical evolutionary optimization techniques such as genetic algorithms, differential evolution, swarm optimization and genetic programming to solve the inverse problem of parameter identification of dynamical systems leading to chaotic states has been gaining popularity in recent years. In this paper, three popular evolutionary algorithms — differential evolution, particle swarm optimization and the firefly algorithm are used for parameter identification of a clearance-coupled-impact oscillator system. The behavior of impacting systems is highly nonlinear exhibiting a myriad of harmonic, low order and high order sub-harmonic resonances, as well as chaotic vibrations. The time-history simulations of the single-degree-of-freedom impact oscillator were obtained by the Neumark-β numerical integration algorithm. The results are illustrated by bifurcation graphs, state space portraits and Poincare’ maps which gives valuable insights on the dynamics of the impact system. The parameter identification problem relates to finding one set of system parameters given a chaotic or periodic system response as a set of Poincaré points and a different but known set of system parameters. The three evolutionary algorithms are compared over a set of parameter identification problems. The algorithms are compared based on solution quality to evaluate the efficacy of using one algorithm over another.

scholarly journals Implementation Aspects Regarding Closed-Loop Control Systems Using Evolutionary Algorithms

Inventions ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 53
Author(s):  
Viorel Minzu ◽  
Saïd Riahi ◽  
Eugen Rusu
Keyword(s):  
Optimal Control ◽  
Evolutionary Algorithms ◽  
Optimal Control Problem ◽  
Control Problem ◽  
Closed Loop ◽  
Batch Reactor ◽  
General Structure ◽  
Computational Effort ◽  
Loop Control ◽  
The Impact

When an optimal control problem requires an important computational effort, a metaheuristic algorithm (MA) can be a useful approach. An MA is conceived to solve a specific optimal control problem having a characteristic objective function. This algorithm solely yields only an optimal offline solution. The desideratum to have a closed-loop implementation can be fulfilled through a supplementary “tool”, the Receding Horizon Control (RHC) structure. This paper addresses a particular case and integrates Evolutionary Algorithms into the RHC structure. The main objective is to propose a general harmonization between the Controller of the closed loop and the Evolutionary Algorithm. Some details concerning the implementation of the closed loop and Controller are described. The impact of the RHC’s prediction technique upon the control sequences’ encoding is also analyzed. Two general structure Controllers are proposed, one of them conceived to cope with restrictive time constraints. Practical ideas have been illustrated through a case study: the well-known optimal control of a fed-batch reactor for ethanol production. This time, our implementation achieves a closed-loop solution. The results from the programs and simulation series validate the Controllers, EAs, and the closed-loop structure. Generally speaking, the association between RHC and EA can be a realistic solution to optimal process control.

scholarly journals On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials Is Best

2021 ◽  
Vol 1 (1) ◽  
pp. 1-38
Author(s):  
Dogan Corus ◽  
Andrei Lissovoi ◽  
Pietro S. Oliveto ◽  
Carsten Witt
Keyword(s):  
Steady State ◽  
Evolutionary Algorithms ◽  
High Probability ◽  
Selective Pressure ◽  
Global Optimisation ◽  
Local Optima ◽  
Multidimensional Knapsack ◽  
Parent Selection ◽  
Population Sizes ◽  
The Impact

We analyse the impact of the selective pressure for the global optimisation capabilities of steady-state evolutionary algorithms (EAs). For the standard bimodal benchmark function TwoMax , we rigorously prove that using uniform parent selection leads to exponential runtimes with high probability to locate both optima for the standard ( +1) EA and ( +1) RLS with any polynomial population sizes. However, we prove that selecting the worst individual as parent leads to efficient global optimisation with overwhelming probability for reasonable population sizes. Since always selecting the worst individual may have detrimental effects for escaping from local optima, we consider the performance of stochastic parent selection operators with low selective pressure for a function class called TruncatedTwoMax, where one slope is shorter than the other. An experimental analysis shows that the EAs equipped with inverse tournament selection, where the loser is selected for reproduction and small tournament sizes, globally optimise TwoMax efficiently and effectively escape from local optima of TruncatedTwoMax with high probability. Thus, they identify both optima efficiently while uniform (or stronger) selection fails in theory and in practice. We then show the power of inverse selection on function classes from the literature where populations are essential by providing rigorous proofs or experimental evidence that it outperforms uniform selection equipped with or without a restart strategy. We conclude the article by confirming our theoretical insights with an empirical analysis of the different selective pressures on standard benchmarks of the classical MaxSat and multidimensional knapsack problems.

scholarly journals Comparing Evolutionary Strategies on a Biobjective Cultural Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Carolina Lagos ◽  
Broderick Crawford ◽  
Enrique Cabrera ◽  
Ricardo Soto ◽  
José-Miguel Rubio ◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Facility Location ◽  
Location Problem ◽  
Facility Location Problem ◽  
Evolutionary Strategies ◽  
Cultural Algorithm ◽  
Historical Knowledge ◽  
Uncapacitated Facility Location Problem ◽  
Normative Knowledge ◽  
The Impact

Evolutionary algorithms have been widely used to solve large and complex optimisation problems. Cultural algorithms (CAs) are evolutionary algorithms that have been used to solve both single and, to a less extent, multiobjective optimisation problems. In order to solve these optimisation problems, CAs make use of different strategies such as normative knowledge, historical knowledge, circumstantial knowledge, and among others. In this paper we present a comparison among CAs that make use of different evolutionary strategies; the first one implements a historical knowledge, the second one considers a circumstantial knowledge, and the third one implements a normative knowledge. These CAs are applied on a biobjective uncapacitated facility location problem (BOUFLP), the biobjective version of the well-known uncapacitated facility location problem. To the best of our knowledge, only few articles have applied evolutionary multiobjective algorithms on the BOUFLP and none of those has focused on the impact of the evolutionary strategy on the algorithm performance. Our biobjective cultural algorithm, called BOCA, obtains important improvements when compared to other well-known evolutionary biobjective optimisation algorithms such as PAES and NSGA-II. The conflicting objective functions considered in this study are cost minimisation and coverage maximisation. Solutions obtained by each algorithm are compared using a hypervolume S metric.

scholarly journals On the use of self-* Island-based evolutionary computation methods on complex environments

2018 ◽  
Vol 15 (3) ◽  
pp. 733-750
Author(s):  
Rafael Nogueras ◽  
Carlos Cotta
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Computation ◽  
Self Healing ◽  
Use Of Self ◽  
Complex Environments ◽  
Correlated Failures ◽  
Adaptive Capabilities ◽  
Significant Performance ◽  
The Impact ◽  
Node Failures

We consider the use of island-based evolutionary algorithms (EAs) on fault-prone computational settings. More precisely, we consider scenarios plagued with correlated node failures. To this end, we use the sandpile model in order to induce such complex, correlated failures in the system. Several EA variants featuring self-adaptive capabilities aimed to alleviate the impact of node failures are considered, and their performance is studied in both correlated and non-correlated scenarios for increasingly large volatility rates. Simple island-based EAs are shown to have a significant performance degradation in the correlated scenario with respect to its uncorrelated counterpart. Resilience is however much improved via the use of self-? properties (self-scaling and self-healing), which leads to a more gentle degradation profile. The inclusion of self-generation also contributes to boost performance, leading to negligible degradation in the scenarios considered.

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