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Search results for: evolutionary optimization
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Evolutionary optimization of combinational digital circuits with current-mode gates with respect to transistor count
PublicationW artykule przedstawiono metodę ewolucyjnej minimalizacji liczby tranzystorów w cyfrowym układzie kombinacyjnym, zrealizownaym z wykorzystaniem bramek pracujących w trybie prądowym. W zastosowanym algorytmie ewolucyjnym zastosowano chromosomy o budowie wielowarstwowej, przez co zwiększono wydajność optymalizacji. Wyniki otrzymane z wykorzystaniem proponowanej metody zostały porównane z rezultatami osiągniętymi za pomocą map Karnough...
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Preference-based evolutionary multi-objective optimization in ship weather routing
PublicationIn evolutionary multi-objective optimization (EMO) the aim is to find a set of Pareto-optimal solutions. Such approach may be applied to multiple real-life problems, including weather routing (WR) of ships. The route should be optimal in terms of passage time, fuel consumption and safety of crew and cargo while taking into account dynamically changing weather conditions. Additionally it must not violate any navigational constraints...
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Evolutionary design and optimization of combinational digital circuits with respect to transistor count.
PublicationW artykule przedstawiono możliwość wykorzystania algorytmu ewolucyjnego do projektowania i optymalizacji cyfrowych układów kombinacyjnych w odniesieniu do liczby tranzystorów. Zastosowano chromosomy o budowie wielowarstwowej zwiększające wydajność algorytmu. Zaprojektowano, wykorzystując zaproponowaną metodę, cztery układy kombinacyjne o tabelach logicznych wybranych z literatury. Uzyskane wyniki są w wielu przypadkach lepsze...
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The Use of Evolutionary Algorithms for Optimization in the Modern Entrepreneurial Economy: Interdisciplinary Perspective
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Gender approaches to evolutionary multi-objective optimization using pre-selection of criteria
PublicationA novel idea to perform evolutionary computations (ECs) for solving highly dimensional multi-objective optimization (MOO) problems is proposed. Following the general idea of evolution, it is proposed that information about gender is used to distinguish between various groups of objectives and identify the (aggregate) nature of optimality of individuals (solutions). This identification is drawn out of the fitness of individuals...
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Intelligent Optimization of Hard-Turning Parameters Using Evolutionary Algorithms for Smart Manufacturing
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A Multi-Fidelity Surrogate-Model-Assisted Evolutionary Algorithm for Computationally Expensive Optimization Problems
PublicationIntegrating data-driven surrogate models and simulation models of different accuracies (or fideli-ties) in a single algorithm to address computationally expensive global optimization problems has recently attracted considerable attention. However, handling discrepancies between simulation models with multiple fidelities in global optimization is a major challenge. To address it, the two major contributions of this paper include:...
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W-dominance: Tradeoff-inspired dominance relation for preference-based evolutionary multi-objective optimization
PublicationThe paper presents a method of incorporating decision maker preferences into multi-objective meta-heuristics. It is based on tradeoffcoefficients and extends their applicability from bi-objective to multi-objective. The method assumes that a decision maker specifies a priori each objective’s importance as a weight interval. Based on this, w-dominance relation is introduced, which extends Pareto dominance. By replacing reference...
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Application of a modified evolutionary algorithm for the optimization of data acquisition to improve the accuracy of a video-polarimetric system
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PLC-based Implementation of Stochastic Optimization Method in the Form of Evolutionary Strategies for PID, LQR, and MPC Control
PublicationProgrammable logic controllers (PLCs) are usually equipped with only basic direct control algorithms like proportional-integral-derivative (PID). Modules included in engineering software running on a personal computer (PC) are usually used to tune controllers. In this article, an alternative approach is considered, i.e. the development of a stochastic optimizer based on the (μ,λ) evolution strategy (ES) in a PLC. For this purpose,...
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Evolutionary Sets of Safe Ship Trajectories: problem dedicated operators
PublicationThe paper presents the optimization process of the evolutionary sets of safe ship trajectories method, with a focus on its problem-dedicated operators. The method utilizes a customized evolutionary algorithm to solve a constrained optimization problem. This problem is defined as finding a set of cooperating trajectories (a set is an evolutionary individual) of all the ships involved in the encounter situation. The resulting trajectories...
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Evolutionary Sets of Safe Ship Trajectories: improving the method by adjusting evolutionary techniques and parameters
PublicationThe paper presents some of the evolutionary techniques used by the evolutionary sets of safe ship trajectories method. In general, this method utilizes a customized evolutionary algorithm to solve a constrained optimization problem. This problem is defined as finding a set of cooperating trajectories (here the set is an evolutionary individual) of all the ships involved in the encounter situation. The resulting trajectories are...
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Gender approach to multi-objective optimization of detection systems by pre-selection of criteria
PublicationA novel idea of performing evolutionary computations for solving highly-dimensional multi-objective optimization (MOO) problems is proposed. The information about individual genders is applied. This information is drawn out of the fitness of individuals and applied during the parental crossover in the evolutionary multi-objective optimization (EMO) processes. The paper introduces the principles of the genetic-gender approach (GGA)...
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Gender approach to multi-objective optimization of detection systems by pre-selection of criteria
PublicationA novel idea of performing evolutionary computations for solving highly-dimensional multi-objective optimization (MOO) problems is proposed. The information about individual genders is applied. This information is drawn out of the fitness of individuals and applied during the parental crossover in the evolutionary multi-objective optimization (EMO) processes. The paper introduces the principles of the genetic-gender approach (GGA)...
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Gender approach to multi-objective optimization of detection systems by pre-selection of criteria
PublicationA novel idea of performing evolutionary computations for solving highly-dimensional multi-objective optimization (MOO) problems is proposed. The information about individual genders is applied. This information is drawn out of the fitness of individuals and applied during the parental crossover in the evolutionary multi-objective optimization (EMO) processes. The paper introduces the principles of the genetic-gender approach (GGA)...
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On evolutionary computing in multi-ship trajectory planning, Applied Intelligence
PublicationThe paper presents the updated version of Evolutionary Sets of Safe Ship Trajectories: a method which applies evolutionary algorithms and some of the assumptions of game theory to solving ship encounter situations. For given positions and motion parameters of the ships,the method finds a near optimal set of safe trajectories of all ships involved in an encounter. The method works in real time and the solutions must be returned...
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Multi-criterion, evolutionary and quantum decision making in complex systems
PublicationMulti-criterion, evolutionary and quantum decision making supported by the Adaptive Quantum-based Multi-criterion Evolutionary Algorithm (AQMEA) has been considered for distributed complex systems. AQMEA had been developed to the task assignment problem, and then it has been applied to underwater vehicle planning as another benchmark three-criterion optimization problem. For evaluation of a vehicle trajectory three criteria have...
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Evolutionary algorithm and decisional DNA for multiple travelling salesman problem
PublicationIn the real world, it is common to face optimization problems that have two or more objectives that must be optimized at the same time, that are typically explained in different units, and are in conflict with one another. This paper presents a hybrid structure that combines set of experience knowledge structures (SOEKS) and evolutionary algorithms, NSGA-II (Non-dominated Sorting Genetic Algorithm II), to solve multiple optimization...
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Solving highly-dimensional multi-objective optimization problems by means of genetic gender
PublicationPaper presents a computational optimization study using a genetic gender approach for solving multi-objective optimization problems of detection observers. In this methodology the information about an individual gender of all the considered solutions is applied for the purpose of making distinction between different groups of objectives. This information is drawn out of the fitness of individuals and applied during a current parental...
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Solving highly-dimensional multi-objective optimization problems by means of genetic gender
PublicationPaper presents a computational optimization study using a genetic gender approach for solving multi-objective optimization problems of detection observers. In this methodology the information about an individual gender of all the considered solutions is applied for the purpose of making distinction between different groups of objectives. This information is drawn out of the fitness of individuals and applied during a current parental...
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Multiobjective Aerodynamic Optimization by Variable-Fidelity Models and Response Surface Surrogates
PublicationA computationally efficient procedure for multiobjective design optimization with variable-fidelity models and response surface surrogates is presented. The proposed approach uses the multiobjective evolutionary algorithm that works with a fast surrogate model, obtained with kriging interpolation of the low-fidelity model data enhanced by space-mapping correction exploiting a few high-fidelity training points. The initial Pareto...
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Some Optimization Methods for Simulations in Volunteer and Grid Systems
PublicationIn this chapter, some optimization methods have been presented for improving performance of simulations in the volunteer and grid computing system called Comcute. Some issues related to the cloud computing can be solved by presented approaches as well as the Comcute platform can be used to simulate execution of expensive and energy consuming long-term tasks in the cloud environment. In particular, evolutionary algorithms as well...
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Task Assignments in Logistics by Adaptive Multi-Criterion Evolutionary Algorithm with Elitist Selection
PublicationAn evolutionary algorithm with elitist selection has been developed for finding Pareto-optimal task assignments in logistics. A multi-criterion optimization problem has been formulated for finding a set of Pareto- optimal solutions. Three criteria have been applied for evaluation of task assignment: the workload of a bottleneck machine, the cost of machines, and the numerical performance of system. The machine constraints have...
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Decisional DNA and Optimization Problem
PublicationMany researchers have proved that Decisional DNA (DDNA) and Set of Experience Knowledge Structure (SOEKS or SOE) is a technology capable of gathering information and converting it into knowledge to help decision-makers to make precise decisions in many ways. These techniques have a feature to combine with different tools, such as data mining techniques and web crawlers, helping organization collect information from different sources...
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Rotational Design Space Reduction for Cost-Efficient Multi-Objective Antenna Optimization
PublicationCost-efficient multi-objective design of antenna structures is presented. Our approach is based on design space reduction algorithm using auxiliary single-objective optimization runs and coordinate system rotation. The initial set of Pareto-optimal solutions is obtained by optimizing a response surface approximation model established in the reduced space using coarse-discretization EM simulation data. The optimization engine is...
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Approximate Quality Criteria for Difficult Multi-Objective Optimization Problems
PublicationThis paper introduces approximate analytic quality criteria useful in assessing the efficiency of evolutionary multi-objective optimization (EMO) procedures. We present a summary of extensive research into computing. In the performed comparative study we take into account the various approaches of the state-of-the-art, in order to objectively assess the EMO performance in highly dimensional spaces; where some executive criteria,...
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Approximate Quality Criteria for Difficult Multi-Objective Optimization Problems
PublicationThis paper introduces approximate analytic quality criteria useful in assessing the efficiency of evolutionary multi-objective optimization (EMO) procedures. We present a summary of extensive research into computing. In the performed comparative study we take into account the various approaches of the state-of-the-art, in order to objectively assess the EMO performance in highly dimensional spaces; where some executive criteria,...
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Expedited Multi-Objective Design Optimization of Miniaturized Microwave Structures Using Physics-Based Surrogates
PublicationIn this paper, a methodology for fast multi-objective design optimization of compact microwave circuits is presented. Our approach exploits an equivalent circuit model of the structure under consideration, corrected through implicit and frequency space mapping, then optimized by a multi-objective evolutionary algorithm. The correction/optimization of the surrogate is iterated by design space confinement and segmentation based on...
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Approximate Criteria for the Evaluation of Truly Multi-Dimensional Optimization Problems
PublicationIn this paper we propose new improved approximate quality criteria useful in assessing the efficiency of evolutionary multi-objective optimization (EMO). In the performed comparative study we take into account the various EMO algorithms of the state-of-the-art, in order to objectively assess the EMO performance in highly dimensional spaces. It is well known that useful executive criteria, such as those based on the true Pareto...
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Multicriteria Evolutionary Weather Routing Algorithm in Practice
PublicationThe Multicriteria Evolutionary Weather Routing Algorithm (MEWRA) has already been introduced by the author on earlier TransNav 2009 and 2011 conferences with a focus on theoretical application to a hybrid-propulsion or motor-driven ship. This paper addresses the topic of possible practical weather routing applications of MEWRA. In the paper some practical advantages of utilizing Pareto front as a result of multicriteria optimization...
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Computational intelligence methods in production management
PublicationThis chapter presents a survey of selected computational intelligence methods used in production management. This group of methods includes, among others, approaches based on the artificial neural networks, the evolutionary algorithms, the fuzzy logic systems and the particle swarm optimization mechanisms. From the abovementioned methods particularly noteworthy are the evolutionary and the particle swarm algorithms, which are successfully...
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On deterministic procedures for low-cost multi-objective design optimization of miniaturized impedance matching transformers
PublicationPurpose This paper aims to investigate deterministic strategies for low-cost multi-objective design optimization of compact microwave structures, specifically, impedance matching transformers. The considered methods involve surrogate modeling techniques and variable-fidelity electromagnetic (EM) simulations. In contrary to majority of conventional approaches, they do not rely on population-based metaheuristics, which permit lowering...
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Design space reduction and variable-fidelity EM simulations for feasible Pareto optimization of antennas
PublicationA computationally efficient procedure for multi-objective optimization of antenna structures is presented. In our approach, a response surface approximation (RSA) model created from sampled coarse-discretization EM antenna simulations is utilized to yield an initial set of Pareto-optimal designs using a multi-objective evolutionary algorithm. The final Pareto front representation for the high-fidelity model is obtained using surrogate-based...
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Low-Cost EM-Simulation-Driven Multi-Objective Optimization of Antennas
PublicationA surrogate-based method for efficient multi-objective antenna optimization is presented. Our technique exploits response surface approximation (RSA) model constructed from sampled low-fidelity antenna model (here, obtained through coarse-discretization EM simulation). The RSA model enables fast determination of the best available trade-offs between conflicting design goals. A low-cost RSA model construction is possible through...
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EM-Driven Multi-Objective Optimization of Antenna Structures in Multi-Dimensional Design Spaces
PublicationFeasible multi-objective optimization of antenna structures is presented. An initial set of Pareto optimal solutions is found using a multi-objective evolutionary algorithm (MOEA) working with a fast surrogate antenna model obtained by kriging interpolation of coarse-discretization EM simulation data. To make the surrogate construction computationally feasible in multi-dimensional design space, the space subset containing non-dominated...
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Low-Cost Multi-Objective Optimization Yagi-Uda Antenna in Multi-Dimensional Parameter Space
PublicationA surrogate-based technique for fast multi-objective optimization of a multi-parameter planar Yagi-Uda antenna structure is presented. The proposed method utilizes response surface approximation (RSA) models constructed using training samples obtained from evaluation of the low-fidelity antenna model. Utilization of the RSA models allowsfor fast determination of the best possible trade-offs between conflicting objectives in multi-objective...
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Three-objective antenna optimization by means of kriging surrogates and domain segmentation
PublicationIn this paper, an optimization framework for multi-objective design of antenna structures is discussed which exploits data-driven surrogates, a multi-objective evolutionary algorithm, response correction techniques for design refinement, as well as generalized domain segmentation. The last mechanism is introduced to constrain the design space region subjected to sampling, which permits reduction of the number of training data samples...
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Computationally Efficient Multi-Objective Optimization of and Experimental Validation of Yagi-Uda Antenna
PublicationIn this paper, computationally efficient multi-objective optimization of antenna structures is discussed. As a design case, we consider a multi-parameter planar Yagi-Uda antenna structure, featuring a driven element, three directors, and a feeding structure. Direct optimization of the high-fidelity electromagnetic (EM) antenna model is prohibitive in computational terms. Instead, our design methodology exploits response surface...
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Accelerated multi-objective design optimization of antennas by surrogate modeling and domain segmentation
PublicationMulti-objective optimization yields indispensable information about the best possible design trade-offs of an antenna structure, yet it is challenging if full-wave electromagnetic (EM) analysis is utilized for performance evaluation. The latter is a necessity for majority of contemporary antennas as it is the only way of achieving acceptable modeling accuracy. In this paper, a procedure for accelerated multi-objective design of...
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Genetic Programming for Workload Balancing in the Comcute Grid System
PublicationA genetic programming paradigm is implemented for reliability optimization in the Comcute grid system design. Chromosomes are generated as the program functions and then genetic operators are applied for finding Pareto-suboptimal task assignment and scheduling. Results are compared with outcomes obtained by an adaptive evolutionary algorithm.
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Design Space Reduction for Expedited Multi-Objective Design Optimization of Antennas in Highly-Dimensional Spaces
PublicationA surrogate-based technique for efficient multi-objective antenna optimization is discussed. Our approach exploits response surface approximation (RSA) model constructed from low-fidelity antenna model data (here, obtained through coarse-discretization electromagnetic simulations). The RSA model enables fast determination of the best available trade-offs between conflicting design goals. The cost of RSA model construction for multi-parameter...
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Rapid Surrogate-Aided Multi-Criterial Optimization of Compact Microwave Passives Employing Machine Learning and ANNs
PublicationThis article introduces an innovative method for achieving low-cost and reliable multi-objective optimization (MO) of microwave passive circuits. The technique capitalizes on the attributes of surrogate models, specifically artificial neural networks (ANNs), and multi-resolution electromagnetic (EM) analysis. We integrate the search process into a machine learning (ML) framework, where each iteration produces multiple infill points...
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Expedited constrained multi-objective aerodynamic shape optimization by means of physics-based surrogates
PublicationIn the paper, computationally efficient constrained multi-objective design optimization of transonic airfoil profiles is considered. Our methodology focuses on fixed-lift design aimed at finding the best possible trade-offs between the two objectives: minimization of the drag coefficient and maximization of the pitching moment. The algorithm presented here exploits the surrogate-based optimization principle, variable-fidelity computational...
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Testing Stability of Digital Filters Using Multimodal Particle Swarm Optimization with Phase Analysis
PublicationIn this paper, a novel meta-heuristic method for evaluation of digital filter stability is presented. The proposed method is very general because it allows one to evaluate stability of systems whose characteristic equations are not based on polynomials. The method combines an efficient evolutionary algorithm represented by the particle swarm optimization and the phase analysis of a complex function in the characteristic equation....
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Rapid Simulation-Driven Multiobjective Design Optimization of Decomposable Compact Microwave Passives
PublicationIn this paper, a methodology for fast multiobjective optimization of the miniaturized microwave passives has been presented. Our approach is applicable to circuits that can be decomposed into individual cells [e.g., compact microstrip resonant cells (CMRCs)]. The structures are individually modeled using their corresponding equivalent circuits and aligned with their accurate, EM simulated...
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Multi-objective design optimization of antennas for reflection, size, and gain variability using kriging surrogates and generalized domain segmentation
PublicationCost-efficient multi-objective design optimization of antennas is presented. The framework exploits auxiliary data-driven surrogates, a multi-objective evolutionary algorithm for initial Pareto front identification, response correction techniques for design refinement, as well as generalized domain segmentation. The purpose of this last mechanism is to reduce the volume of the design space region that needs to be sampled in order...
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Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects
PublicationMachine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects Hammed A. Mojeed & Rafal Szlapczynski Conference paper First Online: 14 September 2023 161 Accesses Part of the Lecture Notes in Computer Science book series (LNAI,volume 14125) Abstract Software development project requires proper planning to mitigate risk and...
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Fast Multi-Objective Antenna Design Through Variable-Fidelity EM Simulations
PublicationA technique for fast multi-objective antenna optimization is introduced. A kriging interpolation surrogate constructed from sampled coarse-mesh EM simulations is utilized by multi-objective evolutionary algorithm (MOEA) to obtain the initial Pareto front approximation. The surrogate is defined in a subset of the original design space, determined by means of independently optimized individual objectives. Response correction techniques...
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Efficient Multi-Objective Simulation-Driven Antenna Design Using Co-Kriging
PublicationA methodology for fast multi-objective antenna optimization is presented. Our approach is based on response surface approximation (RSA) modeling and variable-fidelity electromagnetic (EM) simulations. In the design process, a computationally cheap RSA surrogate model constructed from sampled coarse-discretization EM antenna simulations is optimized using a multi-objective evolutionary algorithm. The initially determined Pareto...
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Advanced Ship Control Methods
PublicationThe chapter presents two main streams of research in vessel control at sea: dynamic positioning (DP) of the vessel and decision support in case of collision at sea. The control structure and basic requirements for the DP system are defined. Selected issues of automatic control of a dynamically positioned vessel are discussed. A review of advanced methods of controlling a DP ship is carried out, taking into account the tasks of...