PID controllers are a reliable, robust, practical and easy to implement control solution for industrial processes. They provide the first control layer for a vast majority of industrial applications. Owing to this, se...
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PID controllers are a reliable, robust, practical and easy to implement control solution for industrial processes. They provide the first control layer for a vast majority of industrial applications. Owing to this, several researches invest time and resources to improve their performance. The research lines in this field scope with new tuning methods, new types of structures and integral design methods. For tuning methods, improvements could be fulfilled stating an optimization problem, which could be non-linear, non-convex and highly constrained. In such instances, evolutionary algorithms have shown a good performance and have been used in various proposals related with PM controllers tuning. This work shows a review of these proposals and the benefits obtained in each case. Some trends and possible future research lines are also identified.
This paper compares three evolutionary computation techniques, namely Steady-State Genetic algorithms, evolutionary Strategies and Differential Evolution for the Unit Commitment Problem. The comparison. is based on a ...
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This paper compares three evolutionary computation techniques, namely Steady-State Genetic algorithms, evolutionary Strategies and Differential Evolution for the Unit Commitment Problem. The comparison. is based on a set of experiments conducted on benchmark datasets as well as on real-world data obtained from the Turkish Interconnected Power System. The results of two state-of-the-art evolutionary approaches, namely a Generational Genetic Algorithm and a Memetic Algorithm for the same benchmark datasets are also included in. the paper for comparison. The tests show that Differential Evolution is the best performer among all approaches on the test data used in the paper. The performances of the other two evolutionary algorithms are also comparable to Differential Evolution. and the results of the algorithms taken from literature showing that all EA approaches tested here are applicable to the Unit Commitment Problem. The results of this experimental study are very promising and promote further study.
Automatic cell planning (ACP) is an optimization problem from the mobile telecommunications domain that addresses finding the location of the network antennae as well as their parameter settings in order to satisfy se...
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Automatic cell planning (ACP) is an optimization problem from the mobile telecommunications domain that addresses finding the location of the network antennae as well as their parameter settings in order to satisfy several cellular operator requirements. Due to its NP-hard complexity, evolutionary techniques have become popular for solving ACP instances. This article presents a survey of evolutionary algorithms (EAs) engineered for addressing ACP problems, analysing both the features of the considered ACP problem and the main aspects of the EAs used to solve them. The survey provides an up-to-date overview that is not limited to any particular kind of evolutionary approach, and comprises advanced algorithmic enhancements like hybridization and parallelization. The article ends by addressing some important issues and open questions that can be the subject of future research.
This paper presents the application of two classes of evolutionary algorithms (EA) to determine optimum design of Single-Phase Switched Reluctance Machine (SPSRM). The EA used is Genetic algorithms (GA) and Differenti...
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This paper presents the application of two classes of evolutionary algorithms (EA) to determine optimum design of Single-Phase Switched Reluctance Machine (SPSRM). The EA used is Genetic algorithms (GA) and Differential Evolution (DE). Due to sensitivity of the output torque to the stator and rotor pole arcs, these are selected as design variables for a multi-objective optimization with the objective of maximizing average torque and torque density, and minimizing copper loss. The proposed optimization is tested on a 4/4 1,25 kW SPSRM, and the results of both algorithms are compared. The performance of the optimized motor is compared to the initial motor through the finite element analysis. The results show improvement in both efficiency and output torque.
The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has em...
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The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts. (C) 2014 Elsevier Ltd. All rights reserved.
This paper Surveys the research on evolutionary algorithms for the Vehicle Routing Problem with Time Windows (VRPTW). The VRPTW can be described as the problem of designing least cost routes from a single depot to a s...
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This paper Surveys the research on evolutionary algorithms for the Vehicle Routing Problem with Time Windows (VRPTW). The VRPTW can be described as the problem of designing least cost routes from a single depot to a set of geographically scattered points. The routes must be designed in such a way that each point is visited only once by exactly one vehicle within a given time interval. All routes start and end at the depot. and the total demands, of all points on one particular route most not exceed the capacity of the vehicle. The main types of evolutionary algorithms for the VRPTW are genetic algorithms and evolution strategies. In addition to describing the basic features of each method, experimental results for the benchmark test problems of Solomon (1987) and Gehring and Homberger (1999) are presented and analyzed.
Automatic test data generation is a very popular domain in the field of search-based software engineering. Traditionally, the main goal has been to maximize coverage. However, other objectives can be defined, such as ...
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Automatic test data generation is a very popular domain in the field of search-based software engineering. Traditionally, the main goal has been to maximize coverage. However, other objectives can be defined, such as the oracle cost, which is the cost of executing the entire test suite and the cost of checking the system behavior. Indeed, in very large software systems, the cost spent to test the system can be an issue, and then it makes sense by considering two conflicting objectives: maximizing the coverage and minimizing the oracle cost. This is what we did in this paper. We mainly compared two approaches to deal with the multi-objective test data generation problem: a direct multi-objective approach and a combination of a mono-objective algorithm together with multi-objective test case selection optimization. Concretely, in this work, we used four state-of-the-art multi-objective algorithms and two mono-objective evolutionary algorithms followed by a multi-objective test case selection based on Pareto efficiency. The experimental analysis compares these techniques on two different benchmarks. The first one is composed of 800 Java programs created through a program generator. The second benchmark is composed of 13 real programs extracted from the literature. In the direct multi-objective approach, the results indicate that the oracle cost can be properly optimized;however, the full branch coverage of the system poses a great challenge. Regarding the mono-objective algorithms, although they need a second phase of test case selection for reducing the oracle cost, they are very effective in maximizing the branch coverage. Copyright (c) 2011 John Wiley & Sons, Ltd.
Recently, it has been proven that evolutionary algorithms produce good results for a wide range of combinatorial optimization problems. Some of the considered problems are tackled by evolutionary algorithms that use a...
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Recently, it has been proven that evolutionary algorithms produce good results for a wide range of combinatorial optimization problems. Some of the considered problems are tackled by evolutionary algorithms that use a representation which enables them to construct solutions in a dynamic programming fashion. We take a general approach and relate the construction of such algorithms to the development of algorithms using dynamic programming techniques. Thereby, we give general guidelines on how to develop evolutionary algorithms that have the additional ability of carrying out dynamic programming steps. Finally, we show that for a wide class of the so-called DP-benevolent problems (which are known to admit FPTAS) there exists a fully polynomial-time randomized approximation scheme based on an evolutionary algorithm. (C) 2011 Elsevier B.V. All rights reserved.
This work describes the application of subgroup discovery using evolutionary algorithms to the usage data of the Moodle Course management system, a case study of the University of Cordoba, Spain. The objective is to o...
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This work describes the application of subgroup discovery using evolutionary algorithms to the usage data of the Moodle Course management system, a case study of the University of Cordoba, Spain. The objective is to obtain rules which describe relationships between the student's usage of the different activities and modules provided by this c-learning system and the final marks obtained in the courses. We use an evolutionary algorithm for the induction of fuzzy rules in canonical form and disjunctive normal form. The results obtained by different algorithms for subgroup discovery are compared, showing the suitability of the evolutionary subgroup discovery to this problem. (c) 2007 Elsevier Ltd. All rights reserved.
This paper addresses the problem of trading off between the minimization of program and data memory requirements of single-processor implementations of dataflow programs. Based on the formal model of synchronous dataf...
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This paper addresses the problem of trading off between the minimization of program and data memory requirements of single-processor implementations of dataflow programs. Based on the formal model of synchronous dataflow (SDF) graphs [1]1 so-called single appearance schedules are known to be program-memory optimal. Among these schedules, buffer memory schedules are investigated and explored based on a two-step approach: I) an evolutionary algorithm (EA) is applied to efficiently explore the (in general) exponential search space of actor firing orders;2) For each order, the buffer costs are evaluated by applying a dynamic programming post-optimization step (GDPPO). This iterative approach is compared to existing heuristics for buffer memory optimization.
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