Optimisation in changing environments is a challenging research topic since many real-world problems are inherently dynamic. Inspired by the natural evolution process, evolutionary algorithms (EAs) are among the most ...
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Optimisation in changing environments is a challenging research topic since many real-world problems are inherently dynamic. Inspired by the natural evolution process, evolutionary algorithms (EAs) are among the most successful and promising approaches that have addressed dynamic optimisation problems. However, managing the exploration/exploitation trade-off in EAs is still a prevalent issue, and this is due to the difficulties associated with the control and measurement of such a behaviour. The proposal of this paper is to achieve a balance between exploration and exploitation in an explicit manner. The idea is to use two equally sized populations: the first one performs exploration while the second one is responsible for exploitation. These tasks are alternated from one generation to the next one in a regular pattern, so as to obtain a balanced search engine. Besides, we reinforce the ability of our algorithm to quickly adapt after cnhanges by means of a memory of past solutions. Such a combination aims to restrain the premature convergence, to broaden the search area, and to speed up the optimisation. We show through computational experiments, and based on a series of dynamic problems and many performance measures, that our approach improves the performance of EAs and outperforms competing algorithms.
The adaptive meta-model evolutionary algorithm has demonstrated its excellent convergence and faster speed than the conventional algorithms in building design evaluations (Xu et al., 2016). However, this algorithm is ...
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A precise estimation of patient length of stay is important for systematically managing both hospital unit resources (medication, equipment, beds) and the distribution of personnel. This is true for hospitalization fo...
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A precise estimation of patient length of stay is important for systematically managing both hospital unit resources (medication, equipment, beds) and the distribution of personnel. This is true for hospitalization following any disease, however the particularities of each trigger a different observation/recovery period. The current study investigates this problem in the context of cancer of the colorectal type on a discrete data set. Several classifiers from distinct conceptual families provide an estimation or even further information on the length of stay of patients that had been operated of cancer in certain stages and invasion at various parts of the colon or rectum. Support vector machines and neural networks give a black box prediction of the hospitalization period, while decision trees and evolutionary algorithms additionally offer the underlying rules of decision. Results are also compared to those of ensemble state-of-the-art techniques: bagging, boosting and random forests. A Wilcoxon rank-sum test demonstrates that the support vector machines, the decision trees and the ensembles are significantly better than the neural networks and the evolutionary algorithms. They also show substantial agreement following Cohen's kappa coefficient to the original outputs. The highest agreement is between the results of support vector machines (SVM)-bagging (0.84) and decision trees (DT)-bagging (0.87). A potential SVM-EA tandem is also investigated, as a more collaborative means towards supporting decision making;its accuracy came similar to that of the plain EA. Faced with the results of each, the professional is given a manner of how to interpret the amalgam of computational opinions and justifications given in support of his/her decision.
This paper evaluates the applicability of different multi-objective optimization methods for environmentally conscious supply chain design. We analyze a case study with three objectives: costs, CO2 and fine dust (also...
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This paper evaluates the applicability of different multi-objective optimization methods for environmentally conscious supply chain design. We analyze a case study with three objectives: costs, CO2 and fine dust (also known as PM - Particulate Matters) emissions. We approximate the Pareto front using the weighted sum and epsilon constraint scalarization methods with pre-defined or adaptively selected parameters, two popular evolutionary algorithms, SPEA2 and NSGA-II, with different selection strategies, and their interactive counterparts that incorporate Decision Maker's (DM's) indirect preferences into the search process. Within this case study, the CO2 emissions could be lowered significantly by accepting a marginal increase of costs over their global minimum. NSGA-II and SPEA2 enabled faster estimation of the Pareto front, but produced significantly worse solutions than the exact optimization methods. The interactive methods outperformed their a posteriori counterparts, and could discover solutions corresponding better to the DM preferences. In addition, by adjusting appropriately the elicitation interval and starting generation of the elicitation, the number of pairwise comparisons needed by the interactive evolutionary methods to construct a satisfactory solution could be decreased. (C) 2016 Elsevier Ltd. All rights reserved.
In recent years, most of products consist of lots of parts in order to satisfy customer demand. Assembly sequence planning becomes more complicated in the product development process than before. A suitable assembly s...
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In recent years, most of products consist of lots of parts in order to satisfy customer demand. Assembly sequence planning becomes more complicated in the product development process than before. A suitable assembly sequence has an effect on the reduction of time and cost in the manufacturing process. This research proposes a new evolutionary algorithm for finding an optimal assembly sequence of products with a large number of parts with deferent release times. The experimental results demonstrate that the new method is superior to the previous method from the viewpoint of the minimization of the make-span.
Swarm intelligence (SI) optimization algorithms are fast and robust global optimization methods, and have attracted significant attention due to their ability to solve complex optimization problems. The underlying ide...
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Swarm intelligence (SI) optimization algorithms are fast and robust global optimization methods, and have attracted significant attention due to their ability to solve complex optimization problems. The underlying idea behind all SI algorithms is similar, and various SI algorithms differ only in their details. In this paper we discuss the algorithmic equivalence of particle swarm optimization (PSO) and various other newer SI algorithms, including the shuffled frog leaping algorithm (SFLA), the group search optimizer (GSO), the firefly algorithm (FA), artificial bee colony algorithm (ABC) and the gravitational search algorithm (GSA). We find that the original versions of SFLA, GSO, FA, ABC, and GSA, are all algorithmically identical to PSO under certain conditions. We discuss their diverse biological motivations and algorithmic details as typically implemented, and show how their differences enhance the diversity of SI research and application. Then we numerically compare SFLA, GSO, FA, ABC, and GSA, with basic and advanced versions on some continuous benchmark functions and combinatorial knapsack problems. Empirical results show that an advanced version of ABC performs best on the continuous benchmark functions, and advanced versions of SFLA and GSA perform best on the combinatorial knapsack problems. We conclude that although these SI algorithms are conceptually equivalent, their implementation details result in notably different performance levels.
The paper is dealing with a current trend of development in distribution networks' control, with the focus on a real-time data acquisition and processing of information providing an overview of the distribution sy...
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The paper is dealing with a current trend of development in distribution networks' control, with the focus on a real-time data acquisition and processing of information providing an overview of the distribution system operation. Nowadays, the amount of information available for the control of the distribution network is very limited, making the use of new sophisticated control algorithms and strategies very difficult or even impossible. The installation of smart meters and intelligent monitoring systems on the low voltage side of distribution transformers opens new possibilities for using specific tools to improve different aspects of the distribution system operation, e.g., a voltage control, a reactive power control, a reduction in losses or a fault location. The use of such systems may lead to a higher utilization of renewable energy sources to the distribution networks.
Agricultural production has become a key factor for the stability of the world economy. The use of pesticides provides a more favorable environment for the crops in agricultural production. However, the uncontrolled a...
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Agricultural production has become a key factor for the stability of the world economy. The use of pesticides provides a more favorable environment for the crops in agricultural production. However, the uncontrolled and inappropriate use of pesticides affect the environment by polluting preserved areas and damaging ecosystems. In the precision agriculture literature, several authors have proposed solutions based on Unmanned Aerial Vehicles (UAVs) and Wireless Sensor Networks (WSNs) for developing spraying processes that are safer and more precise than the use of manned agricultural aircraft. However, the static configuration usually adopted in these proposals makes them inefficient in environments with changing weather conditions (e.g. sudden changes of wind speed and direction). To overcome this deficiency, this paper proposes a computer-based system that is able to autonomously adapt the UAV control rules, while keeping precise pesticide deposition on the target fields. Different versions of the proposal, with autonomously route adaptation metaheuristics based on Genetic algorithms, Particle Swarm Optimization, Simulated Annealing and Hill-Climbing for optimizing the intensity of route changes are evaluated in this study. Additionally, this study evaluates the use of a ground control station and an embedded hardware to run the route adaptation metaheuristics. Experimental results show that the proposed computer-based system approach with autonomous route change metaheuristics provides more precise changes in the UAV's flight route, with more accurate deposition of the pesticide and less environmental damage. (C) 2017 Elsevier B.V. All rights reserved.
In this paper we investigate the efficiency of two immunological algorithms (CLONALG and opt-IA) in the evolution of Boolean functions suitable for use in cryptography. Although in its nature a combinatorial problem, ...
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In this paper we investigate the efficiency of two immunological algorithms (CLONALG and opt-IA) in the evolution of Boolean functions suitable for use in cryptography. Although in its nature a combinatorial problem, we experiment with two representations of solutions, namely, the bitstring and the floating point based representation. The immunological algorithms are compared with two commonly used evolutionary algorithms genetic algorithm and evolution strategy. To thoroughly investigate these algorithms and representations, we use four different fitness functions that differ in the number of parameters and difficulty. Our results indicate that for smaller dimensions immunological algorithms behave comparable with evolutionary algorithms, while for the larger dimensions their performance is somewhat worse. When considering only immunological algorithms, opt-IA outperforms CLONALG in most of the experiments. The difference in the representation for those algorithms is also clear where floating point works better with smaller problem sizes and bitstring representation works better for larger Boolean functions.
Aerothermoelasticity plays a vital role in the design of hypersonic aircraft as the coupling between the thermodynamics, aerodynamics and structural dynamics cannot be ignored. While topology optimization has been use...
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Aerothermoelasticity plays a vital role in the design of hypersonic aircraft as the coupling between the thermodynamics, aerodynamics and structural dynamics cannot be ignored. While topology optimization has been used in the design of aircraft components, thus far, existing optimization algorithms lack the capability to include aerothermodynamic coupling effects. This article presents an original evolutionary structural topology optimization algorithm that includes hypersonic aerothermoelastic effects. The time-varying temperature distribution is applied through a. conjugate heat transfer analysis integrated in time by an unsteady conduction solver, and is coupled to the aerodynamics, which is calculated by a supersonic vortex lattice method. This article analyses the,effect of fluid thermal-structural interactions on the optimization of a hypersonic transport aircraft wing, by optimizing the wing structure with various degrees of coupling. The coupling of the aerothermodynamics drives the optimization of the structural design and therefore must be considered for hypersonic applications. This new optimization algorithm allows the coupling of the aerothermodynamics to be considered in the early stages of the design, potentially avoiding a costly re-design. (C) 2017 Elsevier Ltd. All rights reserved.
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