The purpose of this research is to investigate the effects of different chaotic maps on the exploration/exploitation capabilities of evolutionary algorithms (EAs). To do so, some well-known chaotic maps are embedded i...
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The purpose of this research is to investigate the effects of different chaotic maps on the exploration/exploitation capabilities of evolutionary algorithms (EAs). To do so, some well-known chaotic maps are embedded into a self-organizing version of EAs. This combination is implemented through using chaotic sequences instead of random parameters of optimization algorithm. However, using a chaos system may result in exceeding of the optimization variables beyond their practical boundaries. In order to cope with such a deficiency, the evolutionary method is equipped with a recent spotlighted technique, known as the boundary constraint handling method, which controls the movements of chromosomes within the feasible solution domain. Such a technique aids the heuristic agents towards the feasible solutions, and thus, abates the undesired effects of the chaotic diversification. In this study, 9 different variants of chaotic maps are considered to precisely investigate different aspects of coupling the chaos phenomenon with the baseline EA, i.e. the convergence, scalability, robustness, performance and complexity. The simulation results reveal that some of the maps (chaotic number generators) are more successful than the others, and thus, can be used to enhance the performance of the standard EA.
This study introduces modelling of multi-adaptive neuro-fuzzy inference system (MANFIS) for predicting the bandwidth and notch frequencies of slotted ultra-wideband (UWB) antennas. Rectangular-shaped printed monopole ...
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This study introduces modelling of multi-adaptive neuro-fuzzy inference system (MANFIS) for predicting the bandwidth and notch frequencies of slotted ultra-wideband (UWB) antennas. Rectangular-shaped printed monopole antennas embedded with U-shaped slots are designed to realise triple band-notch characteristics. A MANFIS model is then developed to predict five output parameters (two cut-off frequency points and three notched frequency points) considering 15 geometrical variables of the designed antennas as the inputs of MANFIS model. Extensive simulation has been performed using HFSS software to generate training and testing data patterns. Two optimisation algorithms, genetic algorithm (GA) and particle swarm optimisation (PSO), are implemented to optimally determine the appropriate values of the fuzzy inference system parameters. For GA-optimised MANFIS model, the percentage error is observed between 1 and 2%, whereas, in PSO-trained MANFIS model, it is observed <1%. The comparative analysis establishes that the PSO-trained MANFIS model precisely predicts the antenna performances. For validating the proposed modelling technique, an optimised configuration of the slotted UWB antenna prototype has been fabricated and characterised.
Black box optimization strategies have been proven to be useful tools for solving complex maintenance optimization problems. There has been a considerable amount of research on the right choice of optimization strateg...
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Black box optimization strategies have been proven to be useful tools for solving complex maintenance optimization problems. There has been a considerable amount of research on the right choice of optimization strategies for finding optimal preventive maintenance schedules. Much less attention is turned to the representation of the schedule to the algorithm. Either the search space is represented as a binary string leading to highly complex combinatorial problem or maintenance operations are defined by regular intervals which may restrict the search space to suboptimal solutions. An adequate representation however is vitally important for result quality. This work presents several nonstandard input representations and compares them to the standard binary representation. An evolutionary algorithm with extensions to handle variable length genomes is used for the comparison. The results demonstrate that two new representations perform better than the binary representation scheme. A second analysis shows that the performance may be even more increased using modified genetic operators. Thus, the choice of alternative representations leads to better results in the same amount of time and without any loss of accuracy. (c) 2006 Elsevier Ltd. All rights reserved.
Maintaining population diversity is critical for multi-objective evolutionary algorithms (MOEAs) to solve many-objective optimization problems (MaOPs). Reference vector guided MOEAs have exhibited superiority in handl...
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Maintaining population diversity is critical for multi-objective evolutionary algorithms (MOEAs) to solve many-objective optimization problems (MaOPs). Reference vector guided MOEAs have exhibited superiority in handling this issue, where a set of well distributed reference points on a unit hyperplane are generated to construct the reference vectors. Nevertheless, the pre-defined reference vectors could not well handle MaOPs with irregular (e.g., convex, concave, degenerate, and discontinuous) Pareto fronts (PFs). In this paper, we propose two new reference vector adaptation strategies, namely Scaling of Reference Vectors (SRV) and Transformation of Solutions Location (TSL), to handle irregular PFs. Particularly, to solve an MaOP with a convex/concave PF, SRV introduces a specific center vector and adjusts the other reference vectors around it by using a scaling function. TSL transforms the location of well-diversified solutions into a set of new reference vectors to handle degenerate/discontinuous PFs. The two strategies are incorporated into three representative MOEAs based on reference vectors and tested on benchmark MaOPs. The comparison studies with other state-of-the-art algorithms demonstrate the efficiency of the new strategies. (C) 2019 Elsevier Inc. All rights reserved.
Multicropping is the practice of growing two or more crops in the same space during a single growing season. Planning rules are mathematical equations that use previous experiences of a water resource system to balanc...
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Multicropping is the practice of growing two or more crops in the same space during a single growing season. Planning rules are mathematical equations that use previous experiences of a water resource system to balance the system's water supply and demand, and calculate multicrop areas in various periods. In this paper, linear and nonlinear planning rules are developed for optimal multicrop irrigation areas associated with reservoir operation policies in a reservoir-irrigation system. Reservoir operations are related to water allocations to each irrigated area by considering inflow and storage volume of the reservoir as the water supply in a monthly operation period. evolutionary algorithms (EAs) can determine optimal multicropping patterns planning rules by considering various mathematical patterns. In this paper, three EAs, namely, (1) genetic algorithm (GA), (2) particle swarm optimization (PSO), and (3) shuffled frog leaping algorithm (SFLA) are employed and compared to maximize the total net benefit of the water resource system by supplying irrigation water for a proposed multicropping pattern over the planning horizon. Results show that the SFLA achieves the best solution, with the maximum value of the objective function in both linear and nonlinear planning rules compared to the GA and PSO. Moreover, the best yield of nonlinear rules is 45.52% better (higher) than that obtained by linear rules. (C) 2013 American Society of Civil Engineers.
Because of increasing transport and trade there is a growing threat of marine invasive species being introduced into regions where they do not presently occur. So that the impacts of such species can be mitigated, it ...
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Because of increasing transport and trade there is a growing threat of marine invasive species being introduced into regions where they do not presently occur. So that the impacts of such species can be mitigated, it is important to predict how individuals, particularly passive dispersers are transported and dispersed in the ocean as well as in coastal regions so that new incursions of potential invasive species are rapidly detected and origins identified. Such predictions also support strategic monitoring, containment and/or eradication programs. To determine factors influencing a passive disperser, around coastal New Zealand, data from the genus Physalia (Cnidaria: Siphonophora) were used. Oceanographic data on wave height and wind direction and records of occurrences of Physalia on swimming beaches throughout the summer season were used to create models using artificial neural networks (ANNs) and Naive Bayesian Classifier (NBC). First, however, redundant and irrelevant data were removed using feature selection of a subset of variables. Two methods for feature selection were compared, one based on the multilayer perceptron and another based on an evolutionary algorithm. The models indicated that New Zealand appears to have two independent systems driven by currents and oceanographic variables that are responsible for the redistribution of Physalia from north of New Zealand and from the Tasman Sea to their subsequent presence in coastal waters. One system is centred in the east coast of northern New Zealand and the other involves a dynamic system that encompasses four other regions on both coasts of the country. Interestingly, the models confirm, molecular data obtained from Physalia in a previous study that identified a similar distribution of systems around New Zealand coastal waters. Additionally, this study demonstrates that the modelling methods used could generate valid hypotheses from noisy and complicated data in a system about which there is little previou
During millions of years, nature has developed patterns and processes with interesting characteristics. They have been used as inspiration for a significant number of innovative models that can be extended to solve co...
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During millions of years, nature has developed patterns and processes with interesting characteristics. They have been used as inspiration for a significant number of innovative models that can be extended to solve complex engineering and mathematical problems. One of the most famous patterns present in nature is the Golden Section (GS). It defines an especial proportion that allows the adequate formation, selection, partition, and replication in several natural phenomena. On the other hand, evolutionary algorithms (EAs) are stochastic optimization methods based on the model of natural evolution. One important process in these schemes is the operation of selection which exerts a strong influence on the performance of their search strategy. Different selection methods have been reported in the literature. However, all of them present an unsatisfactory performance as a consequence of the deficient relations between elitism and diversity of their selection procedures. In this paper, a new selection method for evolutionary computation algorithms is introduced. In the proposed approach, the population is segmented into several groups. Each group involves a certain number of individuals and a probability to be selected, which are determined according to the GS proportion. Therefore, the individuals are divided into categories where each group contains individual with similar quality regarding their fitness values. Since the possibility to choose an element inside the group is the same, the probability of selecting an individual depends exclusively on the group from which it belongs. Under these conditions, the proposed approach defines a better balance between elitism and diversity of the selection strategy. Numerical simulations show that the proposed method achieves the best performance over other selection algorithms, in terms of its solution quality and convergence speed. (C) 2018 Elsevier Ltd. All rights reserved.
This paper proposes a four corners' heuristic for application in evolutionary algorithms (EAs) applied to two-dimensional packing problems. The four corners' (FQ heuristic is specifically designed to increase ...
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This paper proposes a four corners' heuristic for application in evolutionary algorithms (EAs) applied to two-dimensional packing problems. The four corners' (FQ heuristic is specifically designed to increase the search efficiency of EAs. Experiments with the FC heuristic are conducted on 31 problems from the literature both with rotations permitted and without rotations permitted, using two different EA algorithms: a self-adaptive parallel recombinative simulated annealing (PRSA) algorithm, and a self-adaptive genetic algorithm (GA). Results on bin packing problems yield the smallest trim losses we have seen in the published literature;with the FC heuristic, zero trim loss was achieved on problems of up to 97 rectangles. A comparison of the self-adaptive GA to fixed-parameter GAs is presented and the benefits of self-adaption are highlighted. (C) 2006 Elsevier B.V. All rights reserved.
A general review of game-theory based evolutionary algorithms (EAs) is presented in this study. Nash equilibrium, Stackelberg game and Pareto optimality are considered, as game-theoretical basis of the evolutionary al...
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A general review of game-theory based evolutionary algorithms (EAs) is presented in this study. Nash equilibrium, Stackelberg game and Pareto optimality are considered, as game-theoretical basis of the evolutionary algorithm design, and also, as problems solved by evolutionary computation. Applications of game-theory based EAs in computational engineering are listed, with special emphasis in structural optimization and, particularly, in skeletal structures. Additionally, a set of three problems are solved: reconstruction inverse problem, fully stressed design problem and minimum constrained weight, for discrete sizing of frame skeletal structures. We compare panmictic EAs, Nash EAs using 4 different static domain decompositions, including also a new dynamic domain decomposition. Two frame structural test cases of 55 member size and 105 member size are evaluated with the linear stiffness matrix method. Numerical experiments show the efficiency of the Nash EAs approach, confirmed with statistical significance analysis of results, and enhanced with the dynamic domain decomposition.
This study deals with the design of elliptical antenna arrays for specific radiation property using three different evolutionary algorithms;namely, self-adaptive differential evolution method, biogeography based optim...
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This study deals with the design of elliptical antenna arrays for specific radiation property using three different evolutionary algorithms;namely, self-adaptive differential evolution method, biogeography based optimisation method and firefly algorithm. These methods are used to determine an optimum set of positions for uniformly excited elliptical antenna array (EAA) that provides a radiation pattern with optimum side lobe level reduction with the constraint of a fixed major lobe beamwidth. Three examples are investigated;8, 12 and 20 elements EAAs using these evolutionary algorithms. The comparison shows that the design of non-uniform EAAs using evolutionary algorithms presents a good side lobe reduction in the radiation pattern for the optimised design. Furthermore, the BBO method shows somewhat better performance compared with the other two methods.
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