778 research outputs found
A novel iterative optimization algorithm based on dynamic random population
U umjetnoj inteligenciji razvijene su različite heurističke metode optimalizacije. Te su metode uglavnom potaknute prirodnom evolucijom ili nekim primjenljivim inovacijama koje traže dobra (gotovo optimalna) rješenja uz razumnu računalnu cijenu za istraživane probleme. U radu se predlaže novi iterativni algoritam optimalizacije. Algoritam se zasniva na pretraživanju najvrednijeg dijela područja rješenja, koje je uobičajeno koncentrirano oko ciljanog (bias) vektora (u obliku dinamične slučajne populacije). Taj algoritam nezasitno pretražuje prostor rješenja u potrazi za globalnim ekstremom. Usporedba rezultata predloženog algoritma i nekih poznatih heurističkih metoda pretraživanja potvrđuje superiornost naše predložene metode u rješavanju različitih nelinearnih problema optimalizacije sa stajališta jednostavnosti i točnosti.Various heuristic optimization methods have been developed in artificial intelligence. These methods are mostly inspired by natural evolution or some applicable innovations, which seek good (near-optimal) solutions at a reasonable computational cost for search problems. A new iterative optimization algorithm is proposed in this paper. The algorithm is based on searching the most valuable part of the solution space, which is normally concentrated about a targeted bias vector (in the form of a dynamic random population). This algorithm greedily searches the solution space for global extremum. The comparison results between the proposed algorithm and some of the well-known heuristic search methods confirm the superiority of our proposed method in solving various non-linear optimization problems from the viewpoint of simplicity and accuracy
Computational Chemotaxis in Ants and Bacteria over Dynamic Environments
Chemotaxis can be defined as an innate behavioural response by an organism to
a directional stimulus, in which bacteria, and other single-cell or
multicellular organisms direct their movements according to certain chemicals
in their environment. This is important for bacteria to find food (e.g.,
glucose) by swimming towards the highest concentration of food molecules, or to
flee from poisons. Based on self-organized computational approaches and similar
stigmergic concepts we derive a novel swarm intelligent algorithm. What strikes
from these observations is that both eusocial insects as ant colonies and
bacteria have similar natural mechanisms based on stigmergy in order to emerge
coherent and sophisticated patterns of global collective behaviour. Keeping in
mind the above characteristics we will present a simple model to tackle the
collective adaptation of a social swarm based on real ant colony behaviors (SSA
algorithm) for tracking extrema in dynamic environments and highly multimodal
complex functions described in the well-know De Jong test suite. Later, for the
purpose of comparison, a recent model of artificial bacterial foraging (BFOA
algorithm) based on similar stigmergic features is described and analyzed.
Final results indicate that the SSA collective intelligence is able to cope and
quickly adapt to unforeseen situations even when over the same cooperative
foraging period, the community is requested to deal with two different and
contradictory purposes, while outperforming BFOA in adaptive speed. Results
indicate that the present approach deals well in severe Dynamic Optimization
problems.Comment: 8 pages, 6 figures, in CEC 07 - IEEE Congress on Evolutionary
Computation, ISBN 1-4244-1340-0, pp. 1009-1017, Sep. 200
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A survey of swarm intelligence for dynamic optimization: algorithms and applications
Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given
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