Nature inspired optimization algorithms (NOA) are stochastic search methods that simulate how nature solves its own problems. optiSLang provides Evolutionary Algorithms, Particle Swarm Optimization, Stochastic Design Improvement and Covariance Matrix Adaptation. For each of these algorithms a start population is generated randomly using Latin Hypercube Sampling or several designs can be selected from previous analyses. In the ideal case, optimal designs are taken from a preceding sensitivity analysis. If you provide more start designs than in Start population size specified, optiSLang will take them all. Out of this start population as much best designs as needed are chosen to create the next generation.

Evolutionary Algorithms (EA) are stochastic search methods that mimic processes of natural biological evolution like adaption, selection and variation. optiSLang uses a flexible implementation of genetic algorithm (GA) and evolutionary strategy (ES) which allows you to scale between pure GA, which performs a global search, and pure ES, which searches locally. Evolutionary algorithms can be used for discrete and continuous design variables, for single and multi-objective optimization tasks, problems with a large ratio of failed designs. EA are significant more efficient than gradient-based and response surface based methods if the number of design variables and constraint conditions is large.

Particle Swarm Optimization (PSO) is a population based global optimization algorithm, where each population consists of a given number of particles which move through the design space. Based on an initial position, which is chosen out of the start population, the position of each particle is updated in each iteration step with respect to the best global position and its own best position found in previous steps. The PSO works similarly to the Evolutionary Algorithms for single and multi-objective optimization problems and problems with a large amount of failed designs. It is less efficient compared to Evolutionary Algorithms if discrete design variables and many constraint conditions are investigated. For optimization tasks with continuous design variables the PSO converges generally very close to the global optimum.

Stochastic Design Improvement (SDI) is a local, single-objective optimization procedure using a simple stochastic approach. Based on an initial design, which is the best one out of the start population, new samples are generated by Latin Hypercube Sampling within a given range. In each iteration step, the best design is taken as new center point for the sampling of the next iteration. The ranges of the sampling scheme are adapted in each iteration step. Due to its pure stochastic design improvement the SDI is very robust and works for a high amount of failed designs, discrete and continuous parameter, and a high number of design variables and constraint conditions. Nevertheless, its efficiency is significantly lower compared to Evolutionary Algorithms and Particle Swarm Optimization.

Covariance Matrix Adaptation (CMA) is a stochastic, or randomized, evolution strategy for real-parameter (continuous domain) optimization of non-linear, non-convex functions. Randomized search algorithms are regarded to be robust in a rugged search landscape, which can comprise discontinuities, (sharp) ridges, or local optima. The CMA in particular is designed to tackle, additionally, ill-conditioned and non-separable problems. It is a method to update the covariance matrix of the distribution new candidate solutions are sampled according to. In contrast to most classical methods, fewer assumptions on the nature of the underlying objective function are made. Only the ranking between candidate solutions is exploited for learning the sample distribution and neither derivatives nor even the function values themselves are required by the method.

Further information about methods of multidisciplinary optimization used in optiSLang can be found here.

Relevant Parameters

Only parameters with deterministic properties (Optimization or Opt.+Stoch. parameter) are relevant for optimization.

Algorithm Settings

Algorithm Settings

To access the options shown in the following table, double-click the Nature Inspired Optimization system on the Scenery pane and switch to the NOA tab.

Common Settings

Population

This section contains parameters required for initialization and convergence.

Fitness

This section provides parameters which influence the selection mechanism and for constraint handling methods.

Special settings for Evolutionary Algorithm

These settings are available only when you have selected Evolutionary Algorithm as the algorithm type.

Selection

This section provides parameters which influence the parent selection mechanism, and the archive.

Recombination

The crossover operator is a method of recombination where two parent individuals produce two offspring by sharing information between chromosomes. This section provides various parameters for choosing and adjusting the crossover mechanism.

Mutation

Mutation introduces random variation to the genes of the offspring chromosome.

Special Settings for Particle Swarm Optimization

These settings are available only when you have selected Particle Swarm Optimization as the algorithm type.

Swarm Behavior

This section contains various parameters for the swarm movement.

Special Settings for Stochastic Design Improvement

These settings are available only when you have selected Stochastic Design Improvement as the algorithm type.

Special Settings for Covariance Matrix Adaptation

These settings are available only when you have selected Covariance Matrix Adaptation as the algorithm type.

Additional Options

To access the options shown in the following table, in any tab, click Show additional options.