Search

Localisation errors have a great impact on Autonomous Underwater Vehicles (AUVs) as search agents. Different approaches for solving the localisation problem can be used and combined together for greater accuracy in estimating AUVs’ locations. The effect of localisation errors on locating a target can be lightened by designing a search algorithm that avoids extensive use of exact lo-cation information. In this paper, two cooperative search algorithms are proposed and evaluated. In these algorithms, a high-level mechanism is employed for building a global view of the search space using minimum possible search information. These algorithms rely on low-level search algorithms with exploring roles. Particle Swarm Optimisation (PSO) and all-to-one Self-Organising Migrating Algorithm (SOMA) are selected as high-level mechanisms. The conducted experiments demonstrate that both algorithms show a robust behaviour within a range of localisation errors.

This work investigates the locomotion efficiency of snake-like robots through evolutionary optimization using the simulation framework PhysX (NVIDIA). The Genetic Algorithm (GA) is used to find the optimal forward head serpentine gait parameters, and the snake speed is taken into consideration in the optimization. A fitness function covering robot speed is based on a complex physics simulation in PhysX. A general serpenoid form is applied to each joint. Optimal gait parameters are calculated for a virtual model in a simulation environment. The fitness function evaluation uses the Simulation In the Loop (SIL) technique, where the virtual model is an approximation of a real snake-like robot. Experiments were performed using an 8-link snake robot with a given mass and a different body friction. The aim of the optimization was speed and length of the trace.

Tento příspěvek popisuje analytické programování, novou metodu, která umožňuje řešit různé problémy z pole symbolické regrese. Symbolická regrese byla poprvé navržena J.R. Kozou v genetickém programování a C. Ryanem v gramatické evoluci. Tento příspěvek vysvětluje hlavní principy analytického programování a demonstruje jeho schopnost syntetizovat vhodná řešení, zvané programy. Pak je srovnána struktura s genetickým programováním a gramatickou evolucí. Po teoretické části, komparativní studie zaměřená na Boolovské problémy k-symetrické a k-paritní s Kozovým genetickým programováním a analytickým programováním. Zde jsou použity dva evoluční algoritmy: diferenciální evoluce a samoorganizující se migrační algoritmus. Tato komparativní studie Boolovských k-symetrických a k-paritních problémů je pokračováním předchozí studie provedené analytickým programováním v minulosti., This contribution introduces analytical programming, a novel method that allows solving various problems from the symbolic regression domain. Symbolic regression was first proposed by J. R. Koza in his genetic programming and by C. Ryan in grammatical evolution. This contribution explains the main principles of analytic programming, and demonstrates its ability to synthesize suitable solutions, called programs. It is then compared in its structure with genetic programming and grammatical evolution. After theoretical part, a comparative study concerned with Boolean k-symmetry and k-even problems from Koza's genetic programming domain is done with analytical programming. Here, two evolutionary algorithms are used with analytical programming: differential evolution and self-organizing migrating algorithm. Boolean k-symmetry and k-even problems comparative study here are continuation of previous comparative studies done by analytic programming in the past.

V tomto článku je porovnáváno chování SOMA, nového stochastického optimalizačního algorimu, se simulovaným žíháním a diferenciální evolucí pro inženýrské aplikace. Tato aplikace je automatickou dedukcí 14 Fourierových členů v radiofrekvenční křivce k nastavení Langmuirovy sondy. Langmuirova sondy jsou diagnostické nástroje používané k určení iontové hustoty a distribuce elektronové energie v plasmových procesech. RF plasmy jsou inherenetní nelineární a mnohy harmonických může být vygenerováno v plasme. RF komponenty napříč iontovým pláštěm okolo sondy zkreslí měření. Pro zlepšení kvality měření, jsou tyto RF komponenty odstraněny aktivní kompenzační metodou. V tomto výzkumu, bylo dosaženo použitím RF signálu na hrot sondy, že souhlasilo oboje, jak fáze tak amplituda RF signálu generované z plasmy. Zde bylo použito 7 harmonických ke generování křivky aplikované na hrot. Proto 14 interaktivních parametrů (7 fází a 7 amplitud) bylo upravováno online. V předchozí studii simulované žíhaní a diferenciální evoluce byly aplikovány úspěšně na tento problém a tak byly vybrány k porovnání s chováním SOMA. V této aplikační doméně, SOMA byla shledána jako překonatel simulovaného žíhání a diferenciální evoluce., In this article, the performance of a self-organizing migration algorithm (SOMA), a new stochastic optimization algorithm, has been compared with simulated annealing (SA) and differential evolution (DE) for an engineering application. This application is the automated deduction of 14 Fourier terms in a radio-frequency (RF) waveform to tune a Langmuir probe. Langmuir probes are diagnostic tools used to determine the ion density and the electron energy distribution in plasma processes. RF plasmas are inherently non-linear, and many harmonics of the driving fundamental can be generated in the plasma. RF components across the ion sheath formed around the probe distort the measurements made. To improve the quality of the measurements, these RF components can be removed by an active-compensation method. In this research, this was achieved by applying an RF signal to the probe tip that matches both the phase and amplitude of the RF signal generated from the plasma. Here, seven harmonics are used to generate the waveform applied to the probe tip. Therefore, 14 mutually interacting parameters (seven phases and seven amplitudes) had to be tuned on-line. In previous work SA and DE were applied successfully to this problem, and hence were chosen to be compared with the performance of SOMA. In this application domain, SOMA was found to outperform SA and DE.

This contribution introduces analytical programming, a novel method that allows solving various problems from the symbolic regression domain. Symbolic regression was firstly proposed by J. R. Koza in his genetic programming and by C. Ryan for grammatical evolution. This contribution explains the main principles of analytic programming, and demonstrates its ability to synthesise suitable solutions, called programs. It is then compared with genetic programming and grammatical evolution. This comparative study is concerned with three Boolean k-symmetry problems from Koza's genetic programming domain, which are solved by means of analytical programming. Here, two evolutionary algorithms are used with analytical programming: differential evolution and self-organizing migrating algorithm.