3,619 research outputs found
Do evolutionary algorithms indeed require random numbers? Extended study
An inherent part of evolutionary algorithms, that are based on Darwin theory of evolution and Mendel theory of genetic heritage, are random processes. In this participation, we discuss whether are random processes really needed in evolutionary algorithms. We use n periodic deterministic processes instead of random number generators and compare performance of evolutionary algorithms powered by those processes and by pseudo-random number generators. Deterministic processes used in this participation are based on deterministic chaos and are used to generate periodical series with different length. Results presented here are numerical demonstration rather than mathematical proofs. We propose that a certain class of deterministic processes can be used instead of random number generators without lowering of evolutionary algorithms performance. © Springer International Publishing Switzerland 2013
Electrical power grid network optimisation by evolutionary computing
A major factor in the consideration of an electrical power network of the scale of a national grid is the calculation of power flow and in particular, optimal power flow. This paper considers such a network, in which distributed generation is used, and examines how the network can be optimized, in terms of transmission line capacity, in order to obtain optimal or at least high-performing configurations, using multi-objective optimisation by evolutionary computing methods
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Handling boundary constraints for particle swarm optimization in high-dimensional search space
Despite the fact that the popular particle swarm optimizer (PSO) is currently being extensively applied to many real-world problems that often have high-dimensional and complex fitness landscapes, the effects of boundary constraints on PSO have not attracted adequate attention in the literature. However, in accordance with the theoretical analysis in [11], our numerical experiments show that particles tend to fly outside of the boundary in the first few iterations at a very high probability in high-dimensional search spaces. Consequently, the method used to handle boundary violations is critical to the performance of PSO. In this study, we reveal that the widely used random and absorbing bound-handling schemes may paralyze PSO for high-dimensional and complex problems. We also explore in detail the distinct mechanisms responsible for the failures of these two bound-handling schemes. Finally, we suggest that using high-dimensional and complex benchmark functions, such as the composition functions in [19], is a prerequisite to identifying the potential problems in applying PSO to many real-world applications because certain properties of standard benchmark functions make problems inexplicit. © 2011 Elsevier Inc. All rights reserved
Multiobjective optimization of electromagnetic structures based on self-organizing migration
PrĂĄce se zabĂœvĂĄ popisem novĂ©ho stochastickĂ©ho vĂcekriteriĂĄlnĂho optimalizaÄnĂho algoritmu MOSOMA (Multiobjective Self-Organizing Migrating Algorithm). Je zde ukĂĄzĂĄno, ĆŸe algoritmus je schopen ĆeĆĄit nejrĆŻznÄjĆĄĂ typy optimalizaÄnĂch Ășloh (s jakĂœmkoli poÄtem kritĂ©riĂ, s i bez omezujĂcĂch podmĂnek, se spojitĂœm i diskrĂ©tnĂm stavovĂœm prostorem). VĂœsledky algoritmu jsou srovnĂĄny s dalĆĄĂmi bÄĆŸnÄ pouĆŸĂvanĂœmi metodami pro vĂcekriteriĂĄlnĂ optimalizaci na velkĂ© sadÄ testovacĂch Ășloh. Uvedli jsme novou techniku pro vĂœpoÄet metriky rozprostĆenĂ (spread) zaloĆŸenĂ© na hledĂĄnĂ minimĂĄlnĂ kostry grafu (Minimum Spanning Tree) pro problĂ©my majĂcĂ vĂce neĆŸ dvÄ kritĂ©ria. DoporuÄenĂ© hodnoty pro parametry ĆĂdĂcĂ bÄh algoritmu byly urÄeny na zĂĄkladÄ vĂœsledkĆŻ jejich citlivostnĂ analĂœzy. Algoritmus MOSOMA je dĂĄle ĂșspÄĆĄnÄ pouĆŸit pro ĆeĆĄenĂ rĆŻznĂœch nĂĄvrhovĂœch Ășloh z oblasti elektromagnetismu (nĂĄvrh Yagi-Uda antĂ©ny a dielektrickĂœch filtrĆŻ, adaptivnĂ ĆĂzenĂ vyzaĆovanĂ©ho svazku v ÄasovĂ© oblastiâŠ).This thesis describes a novel stochastic multi-objective optimization algorithm called MOSOMA (Multi-Objective Self-Organizing Migrating Algorithm). It is shown that MOSOMA is able to solve various types of multi-objective optimization problems (with any number of objectives, unconstrained or constrained problems, with continuous or discrete decision space). The efficiency of MOSOMA is compared with other commonly used optimization techniques on a large suite of test problems. The new procedure based on finding of minimum spanning tree for computing the spread metric for problems with more than two objectives is proposed. Recommended values of parameters controlling the run of MOSOMA are derived according to their sensitivity analysis. The ability of MOSOMA to solve real-life problems from electromagnetics is shown in a few examples (Yagi-Uda and dielectric filters design, adaptive beam forming in time domainâŠ).
Incremental embodied chaotic exploration of self-organized motor behaviors with proprioceptor adaptation
This paper presents a general and fully dynamic embodied artificial neural system, which incrementally explores and learns motor behaviors through an integrated combination of chaotic search and reflex learning. The former uses adaptive bifurcation to exploit the intrinsic chaotic dynamics arising from neuro-body-environment interactions, while the latter is based around proprioceptor adaptation. The overall iterative search process formed from this combination is shown to have a close relationship to evolutionary methods. The architecture developed here allows realtime goal-directed exploration and learning of the possible motor patterns (e.g., for locomotion) of embodied systems of arbitrary morphology. Examples of its successful application to a simple biomechanical model, a simulated swimming robot, and a simulated quadruped robot are given. The tractability of the biomechanical systems allows detailed analysis of the overall dynamics of the search process. This analysis sheds light on the strong parallels with evolutionary search
Evolutionary Dilemmas in a Social Network
We simulate the prisoner's dilemma and hawk-dove games on a real social
acquaintance network. Using a discrete analogue of replicator dynamics, we show
that surprisingly high levels of cooperation can be achieved, contrary to what
happens in unstructured mixing populations. Moreover, we empirically show that
cooperation in this network is stable with respect to invasion by defectors.Comment: 13 pages, 9 figures; to be published in Lecture Notes in Computer
Science 200
Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System
This paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Both algorithms are combined employing a collaborative strategy with sequential execution, which is called DE-NSGA-II. The DE-NSGA-II takes advantage of the exploration abilities of the multi-objective evolutionary algorithms strengthened with the ability to search global mono-objective optimum of DE, that enhances the capability of finding those extreme solutions of Pareto Optimal Front (POF) difficult to achieve. Numerous experiments and performance comparisons between different evolutionary algorithms were performed on a referent problem for the mono-objective and multi-objective literature, which consists of the design of a double reduction gear train. A preliminary study of the problem, solved in an exhaustive way, discovers the low density of solutions in the vicinity of the optimal solution (mono-objective case) as well as in some areas of the POF of potential interest to a decision maker (multi-objective case). This characteristic of the problem would explain the considerable difficulties for its resolution when exact methods and/or metaheuristics are used, especially in the multi-objective case. However, the DE-NSGA-II framework exceeds these difficulties and obtains the whole POF which significantly improves the few previous multi-objective studies.Fil: MĂ©ndez Babey, MĂĄximo. Universidad de Las Palmas de Gran Canaria; EspañaFil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Departamento de IngenierĂa; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - BahĂa Blanca. Instituto de MatemĂĄtica BahĂa Blanca. Universidad Nacional del Sur. Departamento de MatemĂĄtica. Instituto de MatemĂĄtica BahĂa Blanca; ArgentinaFil: GonzĂĄlez, Begoña. Universidad de Las Palmas de Gran Canaria; EspañaFil: Frutos, Mariano. Universidad Nacional del Sur. Departamento de IngenierĂa; Argentina. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - BahĂa Blanca. Instituto de Investigaciones EconĂłmicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de EconomĂa. Instituto de Investigaciones EconĂłmicas y Sociales del Sur; Argentin
On testing global optimization algorithms for space trajectory design
In this paper we discuss the procedures to test a global search algorithm applied to a space trajectory design problem. Then, we present some performance indexes that can
be used to evaluate the effectiveness of global optimization algorithms. The performance
indexes are then compared highlighting the actual significance of each one of them. A number of global optimization algorithms are tested on four typical space trajectory design problems. From the results of the proposed testing procedure we infer for each pair
algorithm-problem the relation between the heuristics implemented in the solution algorithm and the main characteristics of the problem under investigation. From this analysis we derive a novel interpretation of some evolutionary heuristics, based on dynamical system
theory and we significantly improve the performance of one of the tested algorithms
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