The Ant Colony Optimization (ACO) metaheuristic is a bio-inspired approach for hard combinatorial op- timization problems for stationary and non-stationaryenvironments. In the ACO metaheuristic, a colony of artificial ants cooperate for finding high quality solu- tions in a reasonable time. An interesting example of a non-stationary combinatorial optimization problem is the Multiple Elevators Problem (MEP) which con- sists in finding a sequence of movements for each ele- vator to perform in a building so that to minimize, for instance, the users waiting average time. Events like the arrival of one new user to the elevator queue or the fault of one elevator dynamically produce changes of state in this problem. A subclass of MEP is the the so called Single Elevator Problem (SEP). In this work, we propose the design of an ACO model for the SEP that can be implemented as an Ant Colony System (ACS). Keywords: Ant Colony Optimization, Single Elevator Problem, Non-stationary Problems, Ant Colony System design.
A few authors have previously studied the influence of the migration poli- cies instationaryenvironments. Cantu-Paz  and Alba et al.  showed the benefits of sending a random individual instead of the best individual. Cur- rent multi-population approaches for DOPs have used migration policies. For instance, Oppacher and Wineberg in , send the elite (best) individuals from colonies subpopulations to a core subpopulation. Other policies used in litera- ture involve a global knowledge of the entire population, like Ursem in , by applying the hill-valley detection mechanism among the best individuals of each subpopulation, named nation. Recently, Park et al.  have used two populations with different evolutionary objectives and, given the inconvenience of normal mi- grations, they applied crossbreeding as a means of information exchange.
Non–stationary, or dynamic, problems change over time. There exist a variety of forms of dynamism. The concept of dynamic environmentsin the context of this paper means that the fitness landscape changes during the run of an evolutionary algorithm. Genetic diversity is crucial to provide the necessary adaptability of the algorithm to changes. Two mechanism of macromutation are incorporated to the algorithm to maintain genetic diversity in the population. The algorithm was tested on a set of dynamic testing functions provided by a dynamic fitness problem generator. The main goal was to determinate the algorithm’s ability to reacting to changes of optimum values that alter their locations, so that the optimum value can still be tracked when dimensional and multimodal scalability in the functions is adjusted. The effectiveness and limitations of the proposed algorithm is discussed from results empirically obtained.
Trojanowski K. in  analyze the efficiency of two mutation operators ap- plied in a clonal selection based optimization algorithm (AIIA) for non-stationary tasks. Both operators use a α−stable random number generator. The author ar- gues that appropriate tuning of the α parameter allows to outperform the results of algorithms with the traditional operators. The algorithms were tested with six environments generated with two test-benchmarks: a Test Case Generator and a Moving Peaks Benchmark.
In general the conditions of an optimization problem changes by one of the following reasons or a combination of both : 1) The objective function changes itself, 2) The constraints change. A change in the objective function appears when the purpose of the problem changes. Here conditions which were considered desirable before can turn out to be undesirable now and vice versa. Changes in constraints, which modify feasibility of solutions, are related to resources and their availability. Changes can be small or big, soft or abrupt, chaotic, etc. When changes are big, abrupt or chaotic the similarity between solutions found so far and the new ones can be worthless. Even under these hard environmentsEvolutionary Computation (EC) offers advantages, which are absent in other heuristics when searching for solutions to non-stationary problems. The main advantage relies in the fact that Evolutionary Algorithms (EAs) keep a population of solutions. Consequently, facing the change, they allow moving from a solution to another one to determine if any of them are of merit to continue the search from them instead of from scratch . Goldberg and Smith , Cobb and Grefenstette  initiated the research related to the behaviour of EAS on dynamic fitness functions between 1987 and 1992. Recently the interest in this area was dramatically incremented , , , , , , , , , and . The following sections are organized as follows. Section 2 presents a definition of dynamic environments studied in this work. Section 3 describes the dynamic test functions used. Section 4 describes the EA. In section 5 the experiments performed are described. In section 6 results are discussed and finally this document shows our conclusions, current and future work.
The numerical finite element program allowed the prediction of internal temperatures of bovine semen/extender placed in a 0.5 ml plastic straw and frozen in nitrogen vapor at -100 ºC and the temperature differences between the center and the periphery of the semen/extender column packed in straw of 1.9 mm internal diameter. Results indicate these temperature gradients at low values of h are minimal, amounting to a maximum of 1.5 ºC, and thus may be neglected for practical purposes. This finding, further confirmed by calculation of the Biot numbers that demonstrated an external controlling resistance, facilitates the interpretation of freezing rates in 0.5 ml plastic straws immersed in nitrogen vapor over liquid nitrogen, a widely used method for cryopreservation of bovine sperm.
The algorithms for acoustic source localization based on PHAT filtering have been profusely used with good results in reverberant and noisy environments. However, there are very few studies that give a formal explanation of their robustness, most of them providing just an empirical validation or showing results on simulated data. In this work we present a novel analytical model for predicting the behavior of both the SRP-PHAT power maps and the GCC-PHAT functions. The results show that they are only affected by the signal bandwidth, the microphone array topology, and the room geometry, being independent of the spectral content of the received signal. The proposed model is shown to be valid in reverberant environments and under far and near field conditions. Using this result, an analysis study on how the aforementioned factors affect the SRP-PHAT power maps is presented providing well supported theoretical and practical considerations. The model validation is based on both synthetic and real data, obtaining in all cases a high accuracy of the model to reproduce the SRP-PHAT power maps, both in anechoic and non- anechoic scenarios, becoming thus an excellent tool to be exploited for the improvement of real world relevant applications related to acoustic localization.
Grasemann and Miikkulainen method is based on a coevolutionary GA that encodes wavelets as a sequence of lifting steps, which is based on the Enforce Sub- Populations (ESP) neuroevolution, that was reported by the same authors in . The evaluation run making combinations of one individual (lifting step) from each sub-population until each individual had been evaluated an average of 10 times. Since this is a highly time consuming evaluation, in order to save time on the evaluation of the resulting wavelet, only a certain percentage of the largest coefficients is used for reconstruction, setting the rest to zero. Exactly, a 16:1 compression ratio was used, so 6.25% of the coefficients are kept for reconstruction. A comparison between the idealized evaluation function and the per- formance on a real transform-coder is shown on their work. Peak signal-to-noise-ratio (PSNR) was the fit- ness figure used as a quality measure after performing the inverse transform. The fitness for each lifting step was accumulated each time it was used.
principles are related to the function of the product, so they can be applied more directly. The absence of direct functional information about the overall characteristics of the design, during the optimization process, could reduce the possibilities of a valid solution. On complex geometries or time consuming optimizations where manufacturing constraints play an important role, more restricted TRIZ interpretations could be the best suited . TRIZ concepts could prove useful in suggesting modification possibilities based on some of the TRIZ innovation principles. Such principles can be identified by generalizing existing design contradictions in the given part to obtain suggestions from a predefined contradiction table. These suggestions should enunciate the modification principle along with a design-oriented example depicted by CAD models. Other inventive principles are of a rather topological nature and therefore may be implemented in CAD systems' assembly modules. In other cases the principles are of a mechanical or physical nature, which also involves the effect of time and other physical parameters such as velocity, force, acceleration, temperature, etc. and may be implemented using multi-body systems. Normally a graphic description along with a picture or drawing depicting the given suggestion is also provided. With these examples, the user would have a much better idea about where and how the shape modification process should be focused. However, the designer has to implement the required modifications on his or her own by editing step by step the actual shapes and topologies based on how he or she understands the recommendation. This is commonly a time-consuming task that avoids the search for better solutions.
ABSTRACT. When nutrients become scarce E. coli cells enter into a non-growth phase known as stationary and develop a multiple-stress resistance state analogue to sporulation in B. subtilis. Morphological changes are observed, including rounded shape, loss of flagella and thickening of the cell wall. General metabolism is re-directed, macro- molecular degradation is increased, and storage and osmoprotection compounds are synthesized. The reorganization of the nucleoid is ac- companied by an overall repression of gene expression, but a subset of genes required for starvation survival become transcribed in a manner dependent on the stationary phase-specific subunit of RNA polymerase (RpoS or σ s ). The regulatory function of σ s seems to be
7. CONCLUSION AND FUTURE WORKS We have modeled the signal strength of indoor and outdoor WLAN environments, by studying the propagation characteristics of both environments, considering fading effects as well as obstacles. In tackling these issues, we took measurements of QoS parameters over a defined distance and compute the pathloss for both environments. An optimized model that predicts the signal quality of WLANs over various distances was then derived from the experiment. Experimental data obtained from the field were used to validate the models. In both environments, we could predict the distance at which wireless signal quality received was optimal. We observed that the link quality (SNR) degrades with distance and other environmental factors. The fading phenomenon was also approached with the aim of proffering a practical solution. This approach was accomplished by monitoring the signal quality coverage (at various distances). As an outlook, we shall investigate indoor localization and the problem of interference.
Best parameter setting was determined by taking previously found results for this problem presented elsewhere . For the experiments discussed here parameters were set as follows. Crossover and muta- tion probabilities fixed at 0.65 and 0.005, respectively. One of the main conclusions from the previous work is that the algorithm keeps evolving still in advanced generations, so a maximum number of gen- erations was fixed at 50000. The population size was fixed at 100 individuals. Elitism was used to re- tain the best individual found so far under each criterion. As optimal values of the makespan were known for each instance of the test suite, the common due date d to determine f 2 ( σ ) values was fixed at
By means of a stylized example of an inventory management problem that is relevant for practice we study whether SDP is a suitable method if a minimal service level constraint applies and demand is non-stationary. The example has a fixed time horizon is six periods. We started to solve a deterministic problem, where the variable demand equals the expected demand in the stochastic cases. Introducing uncertainty through a Uniform distribution and requiring that all demand has to be fulfilled gives a 75% cost increase. Subsequently we introduced an α-service level constraint and a fill rate constraint per period, being minimal service level constraints.
In many cases there is special requirement on the cutting patterns: only orthogonal guillotine cuts are allowed, i.e., pieces may only be cut horizontally or vertically from one border to the one opposite. Furthermore, the number of the stages of such cuts is often limited in real word applications. In some specific applications the pieces have a fixed orientation (not allow rotation). In same real contexts the rotation of elements (usually by 90º) can be accepted in order to generate a better assignments. For example, rotation is not allowed in newspaper paging or when the items to be cut are decorated or corrugated, whereas orientation is free in the case of plain materials and in other contexts. The guillotine constraint is frequently present in cutting problems, due to technological characteristic of automated cutting machines.
By offspring selection, the best children are chosen and become the parents of the next generation. Typically, parent selection in ES is performed randomly with no regard to fitness; survival in ESs simply saves the µ best individuals, which is only based on the relative ordering of their fitness values. Basically, there are two selection strategies for ESs:
If we are trying to incorporate knowledge to the blind evolutionary search process, the main issue here is how to introduce problem-specific knowledge? If optimality conditions for the solutions are known in advance we can restrict the search operating only on solutions which hold these conditions. When optimality conditions are unknown, which is the case here, one of the options is to import this knowledge from solutions that come out of heuristics specifically designed for the problem under consideration. These types of knowledge-based intermediate solutions contain some of the features included in the best (optimal or quasi-optimal) solution at the end of the evolutionary process.
El estudio presentado aquí no tenía como meta obtener resultados totalmente concluyentes. Por el contrario, el objetivo consistía en generar evidencias en una fase temprana que proporcionaran más seguridad al aventurarse en la realización de diseños de investiga- ción más elaborados para indagar sobre este tema. Se puede decir que la limitación más importante tiene que ver con la cuestión de hasta qué punto se pueden generalizar los resultados. Todos los datos se utilizaron para contrastar dos únicos casos; puesto que un gran número de factores no formaron parte de esta in- vestigación (el más destacado, el docente), no es posible llevar a cabo generalización alguna. No obstante, sí que he empleado una amplia gama de indicadores muy diferentes para poner a prueba la hipótesis propuesta, y todos ellos apuntan en la misma dirección. Además, pese a no constituir la meta principal de este proyecto de investigación, los hallazgos están en sintonía con los resulta- dos positivos de los diseños de aula invertida constatados en otras
Marco Dorigo [6,18,19], the father of an special biological inspired algorithms based on studies of ants natural environments and behaviour: Ant Colony Optimization (ACO) and ANTNET. In general, ants leave pheromone trail in the way to the nest and the food location. So movements are based in quantities of pheromone in the paths to follow. As much pheromone located in one path then higher will be the probability to go in the course of that way. There are always of course ants that not follow the most probable path for obtaining new sources of provisions.