Unconstrained Search Research Articles

Multi-objective optimization of PI controller for BLDC motor speed control and energy saving in Electric Vehicles: A constrained swarm-based approach

Proportional Integral Derivative (PID) controllers are widely employed in industrial applications due to their effectiveness, simplicity, and versatility. Within the automotive sector, one notable application of the Proportional Integral (PI) controller is in the realm of electric motors, particularly the Brushless Direct Current (BLDC) motors powering Electric Vehicles (EVs). The precision of speed control over BLDC motors, renowned for their efficiency and reliability, is paramount for optimizing vehicle performance and ensuring energy efficiency. PIDs must be tuned each time they are used in an application to optimize performance. A well-known empirical hit and trial tuning strategy is the Ziegler–Nichols method, which results in sub-optimal performance in the presence of locally optimal solutions of large-dimensional search spaces. Existing Artificial Intelligence methods tailored for PID controller tuning often focus on singular optimization aspects, neglecting potential performance gains achievable through multi-objective considerations. Furthermore, prevalent swarm-based PID optimization methods typically initialize the population randomly without imposing any bounds on input parameters, thus rendering them susceptible to inconsistent, unreliable, sub-optimal solutions in unconstrained search environments. In our research endeavors, we mathematically model the multi-objective optimization of BLDC motor speed control, energy usage, and efficiency using constrained Differential Evolution and Particle Swarm Optimized PID controllers. The Ziegler–Nichols tuning method is used to ascertain the initial PI parameters and define bounds within the input space. Our methodology addresses a critical gap by integrating real-time road gradient data into the EV control system, enhancing precision and minimizing energy consumption, especially during challenging road conditions. The proposed approach significantly improves motor speed control and energy efficiency in EVs. With our proposed swam-based optimization over 30 iterations, MSE decreased by approximately 95.4% (from 3.8834 to 0.1809), while energy consumption reduced by about 3.1% (from 1.015 kWh to 0.984 kWh). These results highlight the effectiveness of our method in enhancing both speed control precision and energy efficiency. In addition, code related data is available at Github.

Reinvestigation of the internal glycan rearrangement of Lewis a and blood group type H1 epitopes.

Protonated ions of fucose-containing oligosaccharides are prone to undergo internal glycan rearrangement which results in chimeric fragments that obfuscate mass-spectrometric analysis. Lack of accessible tools that would facilitate systematic analysis of glycans in the gas phase limits our understanding of this phenomenon. In this work, we use density functional theory modeling to interpret cryogenic IR spectra of Lewis a and blood group type H1 trisaccharides and to establish whether these trisaccharides undergo the rearrangement during gas-phase analysis. Structurally unconstrained search reveals that none of the parent ions constitute a thermodynamic global minimum. In contrast, predicted collision cross sections and anharmonic IR spectra provide a good match to available experimental data which allowed us to conclude that fucose migration does not occur in these antigens. By comparing the predicted structures with those obtained for Lewis x and blood group type H2 epitopes, we demonstrate that the availability of the mobile proton and a large difference in the relative stability of the parent ions and rearrangement products constitute the prerequisites for the rearrangement reaction.

Fixation-related electrical potentials during a free visual search task reveal the timing of visual awareness

It has been repeatedly claimed that emotional faces readily capture attention, and that they may be processed without awareness. Yet some observations cast doubt on these assertions. Part of the problem may lie in the experimental paradigms employed. Here, we used a free viewing visual search task during electroencephalographic recordings, where participants searched for either fearful or neutral facial expressions among distractor expressions. Fixation-related potentials were computed for fearful and neutral targets and the response compared for stimuli consciously reported or not. We showed that awareness was associated with an electrophysiological negativity starting at around 110ms, while emotional expressions were distinguished on the N170 and early posterior negativity only when stimuli were consciously reported. These results suggest that during unconstrained visual search, the earliest electrical correlate of awareness may emerge as early as 110ms, and fixating at an emotional face without reporting it may not produce any unconscious processing.

Differential Evolution for linear equality constraint satisfaction via unconstrained search in the null space

Differential Evolution for linear equality constraint satisfaction via unconstrained search in the null space

Semantic Memory Search and Retrieval in a Novel Cooperative Word Game: A Comparison of Associative and Distributional Semantic Models.

Considerable work during the past two decades has focused on modeling the structure of semantic memory, although the performance of these models in complex and unconstrained semantic tasks remains relatively understudied. We introduce a two-player cooperative word game, Connector (based on the boardgame Codenames), and investigate whether similarity metrics derived from two large databases of human free association norms, the University of South Florida norms and the Small World of Words norms, and two distributional semantic models based on large language corpora (word2vec and GloVe) predict performance in this game. Participant dyads were presented with 20-item word boards with word pairs of varying relatedness. The speaker received a word pair from the board (e.g., exam-algebra) and generated a one-word semantic clue (e.g., math), which was used by the guesser to identify the word pair on the board across three attempts. Response times to generate the clue, as well as accuracy and latencies for the guessed word pair, were strongly predicted by the cosine similarity between word pairs and clues in random walk-based associative models, and to a lesser degree by the distributional models, suggesting that conceptual representations activated during free association were better able to capture search and retrieval processes in the game. Further, the speaker adjusted subsequent clues based on the first attempt by the guesser, who in turn benefited from the adjustment in clues, suggesting a cooperative influence in the game that was effectively captured by both associative and distributional models. These results indicate that both associative and distributional models can capture relatively unconstrained search processes in a cooperative game setting, and Connector is particularly suited to examine communication and semantic search processes.

Multivariable Unconstrained Pattern Search Method for Optimizing Digital PID Controllers Applied to Isolated Forward Converter

Most of the traditional PID tuning methods are heuristic in nature. The heuristic approach-based tuned PID controllers show only nominal performance. In addition, in the case of a digital redesign approach, mapping of the heuristically-designed continuous-time PID controllers into discrete-time PID controllers and in case of the direct digital design approach, mapping of the continuous-time plant (forward converter) into the discrete-time plant, results in frequency distortion (or warping). Besides this, nonlinear elements such as ADC and DAC, and delay in the digital control loop deteriorate the control performance. There is a need to tune conventionally-designed digital controllers to enhance performance. This paper proposes optimized discrete-time PID controllers for a forward DC–DC converter operating in continuous conduction mode (CCM). The considered conventional digital PID controllers designed on the basis of the digital redesign and direct digital approaches are tuned by one of the multivariable unconstrained pattern search methods named Hooke–Jeeves (H–J) search method to ensure excellent output voltage regulation performance against the changes in input voltage and load current. Numerical results show that the H–J-based optimized PID compensated forward converter system shows tremendous improvement in performance compared to its unoptimized counterpart and simulated annealing (SA)-based compensated system, thus justifying the applicability of the H–J method for enhancing the performance.

Hybrid System Falsification Under (In)equality Constraints via Search Space Transformation

The verification of hybrid systems is intrinsically hard, due to the continuous dynamics that leads to infinite search spaces. Therefore, research attempts focused on hybrid system falsification of a black-box model, a technique that aims at finding an input signal violating the desired temporal specification. Main falsification approaches are based on stochastic hill-climbing optimization, that tries to minimize the degree of satisfaction of the temporal specification, given by its robust semantics. However, in the presence of constraints between the inputs, these methods become less effective. In this article, we solve this problem using a search space transformation that first maps points of the unconstrained search space to points of the constrained one, and then defines the fitness of the former ones based on the robustness values of the latter ones. Based on this search space transformation, we propose a falsification approach that performs the search over the unconstrained space, guided by the robustness of the mapped points in the constrained space. We introduce three versions of the proposed approach that differ in the way of selecting the mapped points. Experiments show that the proposed approach outperforms state-of-the-art constrained falsification approaches.

A constrained multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism

Constrained multi-objective optimization problems (CMOPs) are common in real-world engineering application, and are difficult to solve because of the conflicting nature of the objectives and many constraints. Some constrained multi-objective evolutionary algorithms (CMOEAs) have been developed for CMOPs, but they still suffer from the problems of easily getting trapped into local optimal solutions and low convergence. This paper introduces a multi-objective evolutionary algorithm based on decomposition and dynamic constraint-handling mechanism (MOEA/D-DCH) to tackle this issue. Firstly, the dynamic constraint-handling mechanism divides the search modes into the unconstrained search mode and the constrained search mode, which are dynamically adjusted by the generation number and the proportion of feasible solutions in the population. This mechanism could lead to a faster convergence than the traditional constraint-handling mechanisms. For the constrained search mode, an improved epsilon constraint-handling method is used to enhance the diversity of the population. Then, an individual update mechanism based on the best feasible solution of each sub-problem is designed to update the feasible individuals for maintaining the convergence of the feasible solutions. Finally, MOEA/D-DCH dynamically regulates the parameters of the differential evolution operator to enhance the local search ability. Experiments on 21 benchmark test functions are conducted to test MOEA/D-DCH and five other typical CMOEAs. Meanwhile, a real-world problem is employed to evaluate the practical performance of MOEA/D-DCH. MOEA/D-DCH achieves significantly better results than the other five algorithms on most of the test problems. The results indicate the effectiveness and competitiveness of MOEA/D-DCH for solving CMOPs.

On the Efficacy of Ensemble of Constraint Handling Techniques in Self-Adaptive Differential Evolution

Self-adaptive variants of evolutionary algorithms (EAs) tune their parameters on the go by learning from the search history. Adaptive differential evolution with optional external archive (JADE) and self-adaptive differential evolution (SaDE) are two well-known self-adaptive versions of differential evolution (DE). They are both unconstrained search and optimization algorithms. However, if some constraint handling techniques (CHTs) are incorporated in their frameworks, then they can be used to solve constrained optimization problems (COPs). In an early work, an ensemble of constraint handling techniques (ECHT) is probabilistically hybridized with the basic version of DE. The ECHT consists of four different CHTs: superiority of feasible solutions, self-adaptive penalty, ε -constraint handling technique and stochastic ranking. This paper employs ECHT in the selection schemes, where offspring competes with their parents for survival to the next generation, of JADE and SaDE. As a result, JADE-ECHT and SaDE-ECHT are developed, which are the constrained variants of JADE and SaDE. Both algorithms are tested on 24 COPs and the experimental results are collected and compared according to algorithms’ evaluation criteria of CEC’06. Their comparison, in terms of feasibility rate (FR) and success rate (SR), shows that SaDE-ECHT surpasses JADE-ECHT in terms of FR, while JADE-ECHT outperforms SaDE-ECHT in terms of SR.

A Review of Features and Limitations of Existing Scalable Multiobjective Test Suites

In multiobjective optimization, a scalable test problem is one that can be formulated for an arbitrary number of objectives. Scalable test problems evaluate the conceptual foundations of the so-called many-objective evolutionary algorithms. As an important class of problems, scalable test problems should contemplate a wide variety of features allowing us to evaluate and judge specific components of many-objective evolutionary algorithms. This, in fact, should promote the development of new strategies and/or methods in the design of many-objective optimization approaches. For this reason, the study of features and difficulties of this class of problems, plays a salient role in the development of many-objective approaches. As a result, a number of multiobjective scalable test problems have been proposed in recent years. In this paper, we present a review of features and limitations of existing multiobjective test problems formulated in continuous and unconstrained search spaces. We examine some features observed in some test problems which have not been properly discussed before. Additionally, we summarize a list of features and recommendations that should be considered in the design of scalable multiobjective test instances. Then, we preset a review of the state-of-the-art scalable test suites, including their features and limitations according to the recommended guidelines discussed herein. Finally, some possible paths for future research in this area are briefly discussed.

Assessing sources of uncertainty in length-based estimates of body growth in populations of fishes and macroinvertebrates with bootstrapped ELEFAN

The determination of rates of body growth is the first step in many aquatic population studies and fisheries stock assessments. ELEFAN (Electronic LEngth Frequency ANalysis) is a widely used method to fit a growth curve to length-frequency distribution (LFD) data. However, up to now, it was not possible to assess its accuracy or the uncertainty inherent of this method, or to obtain confidence intervals for growth parameters within an unconstrained search space. In this study, experiments were conducted to assess the precision and accuracy of bootstrapped and single-fit ELEFAN-based curve fitting methods, using synthetic LFDs with known input parameters and a real data set of Abra alba shell lengths. The comparison of several types of bootstrap experiments and their outputs (95% confidence intervals and confidence contour plots) provided a first glimpse into the accuracy of modern ELEFAN-based fit methods. The main components of uncertainty (precision and reproducibility of fit algorithms, seed effects, sample size and matrix information content) could be assessed from partial bootstraps. Uncertainty was mainly determined by LFD matrix size (months x size bins), total number of non-zero bins and the sampling of large-sized individuals. A new pseudo-R² index for the goodness-of-fit of von Bertalanffy growth models to LFD data is proposed. For a large, perfect synthetic data set, pseudo-R²Phi’ was very high (88 to 100%), indicating an excellent fit of the growth model. The small Abra alba data set showed a low pseudo-R²Phi’, from to 54% to 68%, indicating the need for more samples (length measurements) and a larger LFD data matrix. New, robust, bootstrap-based methods for curve fitting are presented and discussed. This study demonstrates a promising new path for length-based analyses of growth and mortality in natural populations, which are the basis for a suite of methods that are included in the new fishboot package.

African Buffalo Optimization for Global Optimization

In this study we apply the African buffalo optimization (ABO) to solve benchmark global optimization problems. Such problems which are artificial representation of different search landscapes ranging from unimodal to multimodal, separable to non-separable, constrained to unconstrained search landscapes have become a veritable instrument to test the search capacities of optimization algorithms. After a number of experimental procedures involving 28 benchmark problems, results from ABO prove to be rather competitive leading to the conclusion that it is a worthy addition to the body of swarm intelligence techniques.

Modeling dynamics of small populations in a simple phenotypic evolutionary algorithm. A space of population states approach

The paper presents a theoretical analysis of a simple phenotypic evolutionary algorithm running with the fitness proportional selection and the Gaussian mutation. The space of population states approach is applied to analyze dynamics of small populations evolving in an unconstrained one-dimensional search space. The approach facilitates a study of a global behavior of evolving populations from a macroscopic point of view. Expected trajectories of population states are regarded in landscapes of various types of fitness functions: unimodal and multimodal, symmetrical and asymmetrical. Phenomena of rapid unification of initially diversified populations and diversification of initially homogeneous populations followed by a movement of a cluster-like population towards the neighborhood of an optimum, observed previously for two-element populations, were confirmed. Studies of a dynamical system generated by the expected states revealed period-doubling bifurcations and chaotic behavior of the system which appear for particular values of a mutation strength parameter and specific fitness functions. A time to convergence to the steady state, as an essential indicator of optimization properties of the process, was also analyzed.

Improved monarch butterfly optimization for unconstrained global search and neural network training

This work is a seminal attempt to address the drawbacks of the recently proposed monarch butterfly optimization (MBO) algorithm. This algorithm suffers from premature convergence, which makes it less suitable for solving real-world problems. The position updating of MBO is modified to involve previous solutions in addition to the best solution obtained thus far. To prove the efficiency of the Improved MBO (IMBO), a set of 23 well-known test functions is employed. The statistical results show that IMBO benefits from high local optima avoidance and fast convergence speed which helps this algorithm to outperform basic MBO and another recent variant of this algorithm called greedy strategy and self-adaptive crossover operator MBO (GCMBO). The results of the proposed algorithm are compared with nine other approaches in the literature for verification. The comparative analysis shows that IMBO provides very competitive results and tends to outperform current algorithms. To demonstrate the applicability of IMBO at solving challenging practical problems, it is also employed to train neural networks as well. The IMBO-based trainer is tested on 15 popular classification datasets obtained from the University of California at Irvine (UCI) Machine Learning Repository. The results are compared to a variety of techniques in the literature including the original MBO and GCMBO. It is observed that IMBO improves the learning of neural networks significantly, proving the merits of this algorithm for solving challenging problems.

Tracking Eye Movements during CT Interpretation: Inferences of Reader Performance and Clinical Competency Require Clinically Realistic Procedures for Unconstrained Search.

Tracking Eye Movements during CT Interpretation: Inferences of Reader Performance and Clinical Competency Require Clinically Realistic Procedures for Unconstrained Search.

An Adaptive EM Algorithm for the Maximum Likelihood Estimation of Non-Homogeneous Poisson Process Software Reliability Growth Models

Non-homogeneous Poisson process (NHPP) software reliability growth models (SRGMa) enable quantitative metrics to guide decisions during the software engineering life cycle, including test resource allocation and release planning. However, many SRGM possess complex mathematical forms that make them difficult to apply. Specifically, traditional procedures solve a system of nonlinear equations to identify the numerical parameters that best characterize failure data. Recently, researchers have developed expectation-maximization (EM) algorithms for NHPP SRGM that exhibit better convergence properties and can therefore find maximum likelihood estimates with greater ease.This paper presents an adaptive EM (AEM) algorithm, which combines an earlier EM algorithm for NHPP SRGM with unconstrained search of the model parameter space. Our performance analysis shows that the AEM outperforms state-of-the-art EM algorithms for NHPP SRGM with very strong statistical significance, which is as much as hundreds of times faster on some data sets. Thus, the approach can fit SRGM very quickly. We also incorporate this high performance adaptive EM algorithm into a heuristic nested model selection procedure to objectively select a model of least complexity that best characterizes the failure data. Results indicate this heuristic approach often identifies the model possessing the best model selection criteria.aAcronyms are not pluralized.

An Unconstrained Likelihood Ascent Based Detection Algorithm for Large MIMO Systems

Neighborhood search algorithms have been proposed for detection in large multiple-input multiple-output systems. They iteratively search for the best vector in a fixed neighborhood. A better way could be to look for an update which is not restricted to a fixed neighborhood. Motivated by this, we formulate a problem to maximize the reduction in maximum likelihood (ML) cost and use it to derive an expression for updating the current solution. Using this update and a likelihood function regarding the locations of errors, we propose an unconstrained likelihood ascent search (ULAS) algorithm. ULAS seeks to provide the maximum reduction in ML cost by finding an update which is not restricted to be in a fixed neighborhood. Using simulations, the proposed algorithm has been shown to provide better error performance for uncoded systems than existing algorithms, at lower complexity. We also show that ULAS is amenable to lattice reduction, which helps in obtaining two variants leading to further improvements in performance.

Geometries, stabilities and electronic properties of copper and selenium doped copper clusters: Density functional theory study

The structures properties of Cun+1 and CunSe clusters have been investigated using an unbiased CALYPSO structure searching method. Firstly, an unbiased search relying on several structurally different initial clusters have been undertaken. Subsequently, geometry optimization by means of density functional theory is carried out to determine the relative stability of various candidates for low lying clusters obtained from the unconstrained search. The results shown that the ground state Cu9 cluster is found to prefer a unique and previously unrecognized structure, with the total energies much lower than all structures proposed in the literature so far. The Cu2Se cluster is the most stable geometries for CunSe clusters. Additionally, the calculated HOMO-LUMO gaps ranges from 1.27 to 2.85eV, which make CunSe clusters suitable candidates in photocatalyst materials. Lastly, the molecular orbital energy and density of states; the adaptive natural density partitioning; the electron localization function, localized orbital locator and Mayer Bond order are also studied for the ground state to develop a deeper understanding on the electronic properties.

Technical Note—Trading Off Quick versus Slow Actions in Optimal Search

We consider the search for a target whose precise location is uncertain. The search region is divided into grid cells, and the searcher decides which cell to visit next and whether to search it quickly or slowly. A quick search of a cell containing the target may damage it, resulting in a failed search, or it may locate the target safely. If the target is not in the cell, the search continues over the remaining cells. If a slow search is performed on a cell, then the search ends in failure with a fixed probability regardless of whether or not the target is in that cell (e.g., because of enemy fire while performing the slow search). If the slow search survives this failure possibility, then the search ends in success if the target is in that cell; otherwise, the search continues over the remaining cells. We seek to minimize the probability of the search ending in failure and consider two types of rules for visiting cells: the unconstrained search, in which the searcher may visit cells in any order, and the constrained search, in which the searcher may only visit adjacent cells (e.g., up, down, left, or right of cells already visited). We prove that the optimal policy for the unconstrained search is to search quickly some initial set of cells with the lowest probabilities of containing the target before slowly searching the remaining cells in decreasing order of probabilities. For the special case in which a quick search on a cell containing the target damages it with certainty, the optimal policy is to search all cells slowly, in decreasing order of probabilities. We use the optimal solution of the unconstrained search in a branch-and-bound optimal solution algorithm for the constrained search. For larger instances, we evaluate heuristics and approximate dynamic programming approaches for finding good solutions.

Enabling Constraints: When Constraining Organizational Search Improves Innovative Performance?

I systematically examine how constraining the degrees of freedom during organizational search affects performance. I label such constraints as “enabling,” reflecting the fact that constraints may channel the collective search toward a better performance. In prior research, constraints in the search process that are imposed by strategy or organizational structure are typically associated with tradeoffs. For instance, constraints may improve average performance at the expense of discovering radical new solutions. Modeling enabling constraints as emerging from imperfect strategies that guide agents in the search process, I show a more refined view in which the tradeoffs apply only to a portion of the parameter space. When organizations focus on large problems relative to the expertise of individual agents and the problems are also complex, then constraining the search improves both average performance and the likelihood of discovering radical improvements. For simple and small problems, unconstrained collective search is uniformly superior. The study has implications for the design of effective strategies and organizations.