Solutions In Search Space Research Articles

Scalable Transfer Evolutionary Optimization: Coping With Big Task Instances.

In today's digital world, we are faced with an explosion of data and models produced and manipulated by numerous large-scale cloud-based applications. Under such settings, existing transfer evolutionary optimization (TrEO) frameworks grapple with simultaneously satisfying two important quality attributes, namely: 1) scalability against a growing number of source tasks and 2) online learning agility against sparsity of relevant sources to the target task of interest. Satisfying these attributes shall facilitate practical deployment of transfer optimization to scenarios with big task instances, while curbing the threat of negative transfer. While applications of existing algorithms are limited to tens of source tasks, in this article, we take a quantum leap forward in enabling more than two orders of magnitude scale-up in the number of tasks; that is, we efficiently handle scenarios beyond 1000 source task instances. We devise a novel TrEO framework comprising two co-evolving species for joint evolutions in the space of source knowledge and in the search space of solutions to the target problem. In particular, co-evolution enables the learned knowledge to be orchestrated on the fly, expediting convergence in the target optimization task. We have conducted an extensive series of experiments across a set of practically motivated discrete and continuous optimization examples comprising a large number of source task instances, of which only a small fraction indicate source-target relatedness. The experimental results show that not only does our proposed framework scale efficiently with a growing number of source tasks but is also effective in capturing relevant knowledge against sparsity of related sources, fulfilling the two salient features of scalability and online learning agility.

Three-Dimensional Flight Corridor: An Occupancy Checking Process for Unmanned Aerial Vehicle Motion Planning inside Confined Spaces

To deploy Unmanned Aerial Vehicles (UAVs) inside heterogeneous GPS-denied confined (potentially unknown) spaces, such as those encountered in mining and Urban Search and Rescue (USAR), requires the enhancement of numerous technologies. Of special interest is for UAVs to identify collision-freeSafe Flight Corridors (SFC+) within highly cluttered convex- and non-convex-shaped environments, which requires UAVs to perform advanced flight maneuvers while exploiting their flying capabilities. Within this paper, a novel auxiliary occupancy checking process that augments traditional 3D flight corridor generation is proposed. The 3D flight corridor is established as a topological structure based on a hand-crafted path either derived from a computer-generated environment or provided by the human operator, which captures humans’ preferences and desired flight intentions for the given space. This corridor is formulated as a series of interconnected overlapping convex polyhedra bounded by the perceived environmental geometries, which facilitates the generation of suitable 3D flight paths/trajectories that avoid local minima within the corridor boundaries. An occupancy check algorithm is employed to reduce the search space needed to identify 3D obstacle-free spaces in which their constructed polyhedron geometries are replaced with alternate convex polyhedra. To assess the feasibility and efficiency of the proposed SFC+ methodology, a comparative study is conducted against the Star-Convex Method (SCM), a prominent algorithm in the field. The results reveal the superiority of the proposed SFC+ methodology in terms of its computational efficiency and reduced search space for UAV maneuvering solutions. Various challenging confined-environment scenarios, each with different obstacle densities (confined scenarios), are utilized to verify the obtained outcomes.

Physics-Informed Machine Learning for Surrogate Modeling of Heat Transfer Phenomena

Abstract In this paper, we propose a sparse modeling method for automatically creating a surrogate model for nonlinear time-variant systems from a very small number of time series data with nonconstant time steps. We developed three machine learning methods, namely, (1) a data preprocessing method for considering the correlation between errors, (2) a sequential thresholded non-negative least-squares method based on term size criteria, and (3) a solution space search method involving similarity model classification—to apply sparse identification of nonlinear dynamical systems, as first proposed in 2016, to temperature prediction simulations. The proposed method has the potential for wide application to fields where the concept of equivalent circuits can be applied. The effectiveness of the proposed method was verified using time series data obtained by thermofluid analysis of a power module. Two types of cooling systems were verified: forced air cooling and natural air cooling. The model created from the thermofluid analysis results with fewer than the number of input parameters, predicted multiple test data, including extrapolation, with a mean error of less than 1 K. Because the proposed method can be applied using a very small number of data, has a high extrapolation accuracy, and is easy to interpret, it is expected not only that design parameter can be fine-tuned and actual loads can be taken into account, but also that condition-based maintenance can be realized through real-time simulation.

A Reinforcing-Learning-Driven Artificial Bee Colony Algorithm for Scheduling Jobs and Flexible Maintenance under Learning and Deteriorating Effects

In the last decades, the availability constraint as well as learning and deteriorating effects were introduced into the production scheduling theory to simulate real-world case studies and to overcome the limitation of the classical models. To the best of our knowledge, this paper is the first in the literature to address the permutation flowshop scheduling problem (PFSP) with flexible maintenance under learning and deterioration effects to minimize the makespan. Firstly, we address the PFSP with flexible maintenance and learning effects. Then, the deteriorating effect is also considered. Adaptive artificial bee colony algorithms (ABC) enhanced with Q-learning are proposed, in which the Nawaz–Enscore–Ham (NEH) heuristic and modified NEH heuristics are hybridized with a maintenance insertion heuristic to construct potential integrated initial solutions. Furthermore, a Q-learning (QL)-based neighborhood selection is applied in the employed bees phase to improve the quality of the search space solutions. Computational experiments performed on Taillard’s well-known benchmarks, augmented with both prognostic and health management (PHM) and maintenance data, demonstrate the effectiveness of the proposed QL-driven ABC algorithms.

New cardiovascular disease prediction approach using support vector machine and quantum-behaved particle swarm optimization

Cardiovascular disease (CVD) is one of the leading causes of death worldwide. Early detection of CVD reduces the risk of a heart attack and increases the chance of recovery. The use of angiography to detect CVD is expensive and has negative side effects. In addition, existing CVD diagnostic methods usually achieve low detection rates and reach the best decision after many iterations with low convergence speeds. Therefore, a novel heart disease detection model based on the quantum-behaved particle swarm optimization (QPSO) algorithm and support vector machine (SVM) classification model, namely, QPSO-SVM, was proposed to analyze and predict heart disease risk. First, the data preprocessing was performed by transforming nominal data into numerical data and applying effective scaling techniques. Next, the SVM fitness equation is expressed as an optimization problem and solved using the QPSO to determine the optimal features. Finally, a self-adaptive threshold method for tuning the QPSO-SVM parameters is proposed, which permits it to drop into local minima, and balances between exploration and exploitation in the solution search space. The proposed model is applied to the Cleveland heart disease dataset and compared with state-of-the-art models. The experimental results show that the proposed QPSO-SVM model achieved the best heart-disease-prediction accuracies of 96.31% on the Cleveland heart data set. Furthermore, QPSO-SVM outperforms other state-of-the-art prediction models considered in this research in terms of sensitivity (96.13%), specificity (93.56%), precision (94.23%), and F1 score (0.95%).

Robust Optimization with Interval Uncertainties Using Hybrid State Transition Algorithm

Robust optimization is concerned with finding an optimal solution that is insensitive to uncertainties and has been widely used in solving real-world optimization problems. However, most robust optimization methods suffer from high computational costs and poor convergence. To alleviate the above problems, an improved robust optimization algorithm is proposed. First, to reduce the computational cost, the second-order Taylor series surrogate model is used to approximate the robustness indices. Second, to strengthen the convergence, the state transition algorithm is studied to explore the whole search space for candidate solutions, while sequential quadratic programming is adopted to exploit the local area. Third, to balance the robustness and optimality of candidate solutions, a preference-based selection mechanism is investigated which effectively determines the promising solution. The proposed robust optimization method is applied to obtain the optimal solutions of seven examples that are subject to decision variables and parameter uncertainties. Comparative studies with other robust optimization algorithms (robust genetic algorithm, Kriging metamodel-assisted robust optimization method, etc.) show that the proposed method can obtain accurate and robust solutions with less computational cost.

Mathematical models for the cutting stock with limited open stacks problem

This research is focused on solving the Cutting Stock with Limited Open Stacks Problem (CS-LOSP). The CS-LOSP is an optimization problem which consists of the classical Cutting Stock Problem (CSP) paired with the additional constraint that the maximum number of open stacks from the sequencing of the cutting patterns obtained from the CSP solution is equal or lower than a preset limit. Despite being a problem with great practical importance, the literature lacks models for this problem, and only one-dimensional problems are addressed. In this paper, we propose two integer linear programming formulations for the CS-LOSP that are valid for solving instances of the CSP of any dimension. In order to eliminate symmetrical solutions to the problem, the proposed formulations sequence sets of cutting patterns instead of sequencing the cutting patterns individually, thus, the search space for solutions is reduced. A set of randomly generated instances for the two-dimensional problem is used to perform computational experiments in order to validate the proposed mathematical formulations.

Maintaining Transient Diversity Is a General Principle for Improving Collective Problem Solving

Humans regularly solve complex problems in cooperative teams. A wide range of mechanisms have been identified that improve the quality of solutions achieved by those teams on reaching consensus. We argue that many of these mechanisms work via increasing the transient diversity of solutions while the group attempts to reach a consensus. These mechanisms can operate at the level of individual psychology (e.g., behavioral inertia), interpersonal communication (e.g., transmission noise), or group structure (e.g., sparse social networks). Transient diversity can be increased by widening the search space of possible solutions or by slowing the diffusion of information and delaying consensus. All of these mechanisms increase the quality of the solution at the cost of increased time to reach it. We review specific mechanisms that facilitate transient diversity and synthesize evidence from both empirical studies and diverse formal models—including multiarmed bandits, NK landscapes, cumulative-innovation models, and evolutionary-transmission models. Apparent exceptions to this principle occur primarily when problems are sufficiently simple that they can be solved by mere trial and error or when the incentives of team members are insufficiently aligned. This work has implications for our understanding of collective intelligence, problem solving, innovation, and cumulative cultural evolution.

Constrained Market Share Maximization by Signal-Guided Optimization

With the rapid development of the airline industry, maximizing the market share with a constrained budget is an urgent econometric problem for an airline. We investigate the problem by adjusting flight frequencies on different flight routes. Owing to the large search space of solutions and the difficulty of predicting the market, this problem is in general daunting to solve. This paper proposes a novel two-stage optimization method to address the challenges. On the higher level, we use a signal to guide the optimization process toward a constrained satisfying solution. On the lower level, we consider the consecutive itineraries in real scenarios and model the unseen correlations between routes in itineraries for market share prediction. In theory, we prove the convergence of our optimization approach. In the experiment, we empirically verify the superiority of both our prediction model and optimization approach over existing works with large-scale real-world data. Our code has been released at: https://github.com/codingAndBS/AirlineMarket.

A novel hermit crab optimization algorithm

High-dimensional optimization has numerous potential applications in both academia and industry. It is a major challenge for optimization algorithms to generate very accurate solutions in high-dimensional search spaces. However, traditional search tools are prone to dimensional catastrophes and local optima, thus failing to provide high-precision results. To solve these problems, a novel hermit crab optimization algorithm (the HCOA) is introduced in this paper. Inspired by the group behaviour of hermit crabs, the HCOA combines the optimal search and historical path search to balance the depth and breadth searches. In the experimental section of the paper, the HCOA competes with 5 well-known metaheuristic algorithms in the CEC2017 benchmark functions, which contain 29 functions, with 23 of these ranking first. The state of work BPSO-CM is also chosen to compare with the HCOA, and the competition shows that the HCOA has a better performance in the 100-dimensional test of the CEC2017 benchmark functions. All the experimental results demonstrate that the HCOA presents highly accurate and robust results for high-dimensional optimization problems.

Recommender System Metaheuristic for Optimizing Decision-Making Computation

We implement a novel recommender system (RS) metaheuristic framework within a nonlinear NP-hard decision-making problem, for reducing the solution search space before high-burden computational steps are performed. Our RS-based metaheuristic supports consideration of comprehensive evaluation criteria, including estimations of the potential solution set’s optimality, diversity, and feedback/preference of the end-user, while also being fully compatible with additional established RS evaluation metrics. Compared to prior Operations Research metaheuristics, our RS-based metaheuristic allows for (1) achieving near-optimal solution scores through comprehensive deep learning training, (2) fast metaheuristic parameter inference during solution instantiation trials, and (3) the ability to reuse this trained RS module for traditional RS ranking of final solution options for the end-user. When implementing this RS metaheuristic within an experimental high-dimensionality simulation environment, we see an average 91.7% reduction in computation time against a baseline approach, and solution scores within 9.1% of theoretical optimal scores. A simplified RS metaheuristic technique was also developed in a more realistic decision-making environment dealing with multidomain command and control scenarios, where a significant computation time reduction of 87.5% is also achieved compared with a baseline approach, while maintaining solution scores within 9.5% of theoretical optimal scores.

A Stochastic Simulation Optimization-Based Range Gate Pull-Off Jamming Method

Range gate pull-off (RGPO) jamming is an electronic countermeasure widely used to fool radar tracking systems. Nevertheless, the research on its strategy optimization has only been tepid. Optimization model, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. The algebraic description for the objective function of the optimization of the RGPO jamming strategy is difficult to obtain, and the jamming results are not deterministic under the influence of the input noise. To address these issues, this paper models the generation of the RGPO jamming strategy as a stochastic simulation optimization (SSO) problem, and proposes an optimization algorithm of the RGPO jamming strategy with the help of SSO technologies. We propose the committee based active learning (CAL) assisted optimal computing budget allocation based particle swarm optimization (CALPSO-OCBA) to alleviate the competition between solution space search and candidate solution performance evaluation by embedding CAL into PSO-OCBA. To improve the feasibility of the proposed algorithm, a scoring scheme that does not rely on the internal knowledge of the radar tracking system (i.e., tracking model, tracking method and tracking parameters) is designed. In addition, we use four most widely used tracking problems as the benchmarks for testing the proposed optimization algorithm of the RGPO jamming strategy. Experimental results demonstrate that the proposed algorithm is highly competitive in solving the optimization problem of the RGPO jamming strategy.

A Differentiable Perspective for Multi-View Spectral Clustering With Flexible Extension.

Multi-view clustering aims to discover common patterns from multi-source data, whose generality is remarkable. Compared with traditional methods, deep learning methods are data-driven and have a larger search space for solutions, which may find a better solution to the problem. In addition, more considerations can be introduced by loss functions, so deep models are highly reusable. However, compared with deep learning methods, traditional methods have better interpretability, whose optimization is relatively stable. In this paper, we propose a multi-view spectral clustering model, combining the advantages of traditional methods and deep learning methods. Specifically, we start with the objective function of traditional spectral clustering, perform multi-view extension, and then obtain the traditional optimization process. By partially parameterizing this process, we further design corresponding differentiable modules, and finally construct a complete network structure. The model is interpretable and extensible to a certain extent. Experiments show that the model performs better than other multi-view clustering algorithms, and its semi-supervised classification extension also has excellent performance compared to other algorithms. Further experiments also show the stability and fewer iterations of the model training.

СОВРЕМЕННОЕ СОСТОЯНИЕ БИОЭВРИСТИК: КЛАССИФИКАЦИЯ, БЕНЧМАРКИНГ, ОБЛАСТИ ПРИМЕНЕНИЯ

The purpose of this article is to analyze the current state of research in the field of developmentof algorithms inspired by nature, including categorization, classification, testing, citation,and application areas. A new multi-level classification system based on the following features ispresented: the criterion of conformity to a natural metaphor, structural, behavioral, search, component,and evaluation criteria. The classification of bio heuristics involves the systematic assignmentof each bio heuristics to one and only one class within a system of mutually exclusive andnon-overlapping classes. Categorization allows an objective approach to the choice of bio heuristics.For each bio heuristics there are specific tasks with which it copes well. Knowing these relationshipsis important for the purposeful application of bio heuristics. An example of classificationis considered. It is noted that the most informative classification criterion is the behavioral criterion,the most cited class of bio heuristics are swarm intelligence algorithms, and the most cited bioheuristics is the PSO particle swarm algorithm. Modern approaches to benchmarking of bio heuristicsare presented: discrete and continuous optimization problems, as well as optimization engineeringproblems. There is a tendency to compare the performance of bio heuristics using statisticalhypothesis testing on benchmarks. The tasks successfully solved by bio heuristics in such areasas engineering design, image processing and computer vision, computer networks and communications,energy and energy management, data analysis and machine learning, robotics, medicaldiagnostics are systematized. There is a tendency to hybridize bio heuristics in one optimizer.However, convincing evidence is required that the results compensate for the increase in complexitycompared to individual algorithms. Optimization problems requiring further research are noted:dynamic and stochastic optimization problems; multicriteria optimization problems; multimodaloptimization problems; multidimensional optimization problems; memetic optimizationproblems in which a variety of search algorithms are combined; optimization problems and adaptationof bio heuristics parameter settings to achieve a balance between the convergence rate andthe diversification of the solution search space.

A Scheduling Method for Heterogeneous Signal Processing Platforms Based on Quantum Genetic Algorithm

Currently, many problems such as variable signal resources, complex execution environments, and low efficiency of scheduling algorithms are faced by heterogeneous signal processing platforms. The task scheduling algorithm is one of the key factors that directly affect the performance of the processing platform. In order to solve the problems of low efficiency of task scheduling algorithms and high computational cost of processors, a heterogeneous platform scheduling algorithm based on the quantum genetic algorithm is proposed in this paper. The algorithm constructs a task scheduling model by using a directed acyclic graph. This paper quantifies the mapping relationship between the quantum genetic algorithm and task scheduling. It corresponds qubits to binary, chromosomes to processor numbers, and individuals to processor scheduling strategies. In this paper, a new way of coding chromosomes using quantum coherence properties is designed to reduce the population size and increase population diversity. Crossover operations are performed on all individuals using full-interference crossover to avoid the results falling into local optimal solutions. The population of slow convergence is solved by implementing mutation operations on populations through quantum rotation gates. In addition, a task pre-ordering stage is designed based on the table scheduling algorithm. The task scheduling priority developed at this stage is used as the reference value for the initial encoding of the population, so that the search space for solutions is reduced. Finally, experiments are conducted using randomly generated task graphs. The algorithm is compared with improved genetic algorithms and existing intelligent scheduling algorithms. The results show that the algorithm can still obtain better results when the number of populations and iterations is small. It is more appropriate for heterogeneous platforms and computation-intensive tasks.

Hybrid intelligence strategy for techno-economic reactive power dispatch approach to ensure system security

This work consolidates chaotic maps into a contemporary heuristic approach called Chimp Optimization Algorithm (COA). Augmentation of ten chaotic maps to COA forms the hybrid Chaotic COA (CCOA) which generates solutions in search space with enhanced mobility. A bi-level strategy of validating the robustness of the proposed hybrid method is adopted by first benchmarking it on eighteen assorted benchmark test functions and later by performing statistical analysis. The result obtained candidly establishes that merging of chaotic maps (dominantly Chaotic 4 Iterative type-CCOA4) enhances the performance of COA. The inferences congregated from the benchmark functions and statistical analysis facilitates the application of the recommended algorithm to obtain techno-economic solution in reactive power dispatch (RPD) problem which is manifested as a challenging issue in power system operation. The RPD problem is analyzed implementing the proposed approach with series-shunt compensation in a competitive electricity market for secured operation and the proposed Iterative type-CCOA4 reduces the total operating cost by 3.3450 × 103$ with respect to initial operating cost.

Optimizing human reliability in work shifts scheduling: A real case of clothing job shop

AbstractIn this research, we provide a mathematical model with an objective function of minimizing human errors for the problem of shift scheduling optimization. The unique aspects which are considered in our model are learning, forgetting, fatigue, and rest parameters (which affect the human reliability). Various problems with different human parameters, based on real cases of clothing job shops, are solved to study the efficiency and effectiveness of the optimization model. Two improved bacterial foraging algorithms are presented to handle the complexity of our problem. To examine the capability of the presented swarm intelligence algorithms in obtaining global optimum problem, we compare the results of the algorithms against the solution of basic bacterial foraging algorithm in terms of solutions quality. Results indicate that the solution approaches are more efficient in searching solution space and convergence. Analysis of different shift scheduling shows that in moderate fatigue condition, the scenario of scheduling with continuous work hours is better in terms of productivity. Also, experiments demonstrate that optimum reliable shift schedules are related to human factors. The findings of our research indicate that the presented scheduling model and improved algorithms can provide optimum shift schedules regarding human factors.

Graph colouring using evolutionary computation: A case study of blind naked mole‐rat algorithm

AbstractGraph colouring problem (GCP) is an NP‐complete optimization problem. It is famous for its applications in scheduling, register allocation, and map colouring. In recent years, biological inspired and especially Swarm intelligence (SI) techniques have gained popularity for solving complex optimization problems. In this article, we have proposed blind naked mole rat‐based colouring (BNMR‐Col) for graphs. BNMR‐Col uses both exploitation and exploration to find the best solution in search space. Exploitation uses both local moves and global moves to find a better solution in the surroundings of an existing solution. On the other hand, exploration generates new solution by combining various solutions from the search space. BNMR‐Col shows better convergence rate and approaches the lowest colour value in 83% of the cases when tested on standard benchmark graph instances.

A Matrix-Cube-Based Estimation of Distribution Algorithm for No-Wait Flow-Shop Scheduling With Sequence-Dependent Setup Times and Release Times

The no-wait flow-shop scheduling problem (NFSSP) with sequence-dependent setup times (SDSTs) and release times (RTs) is applicable in many areas, such as steel production, food processing, and chemical processing. Estimation of the distribution algorithm (EDA) has recently been recognized as a prominent metaheuristic methodology in the field of evolutionary computation due to its excellent performance of global exploration. In this article, an innovative matrix-cube-based (i.e., 3-D) EDA (MCEDA) is first proposed to minimize the total earliness and tardiness (TET) of the NFSSP with SDSTs and RTs. This problem is NP-hard in the strong sense. First, a 3-D matrix cube is devised to learn the valuable information from promising solutions or excellent individuals. Second, an EDA model or probabilistic model based on the matrix cube and a special sampling method is presented to perform effective exploration in solution space and find promising regions. Third, based on a series of newly defined subneighborhoods, a new local search with both a speed-up scanning method and one search strategy is developed to execute exploitation from promising regions. Fourth, a speed-up evaluation method based on the problem’s property is designed to reduce the computational complexity for calculating criterion and accelerate the search process. Owing to the reasonable hybridization of exploration and exploitation, MCEDA can perform very efficient search in solution space. Extensive test results on instances of such a just-in-time problem first show that MCEDA can achieve better solution than state-of-the-art algorithms in obviously less computation time. Additional experiments on instances of various NFSSPs further confirm the efficiency and robustness of MCEDA.

Mastering Arterial Traffic Signal Control With Multi-Agent Attention-Based Soft Actor-Critic Model

Recent studies have made dozens of attempts to apply multi-agent deep reinforcement learning (MARL) for large-scale traffic signal control. However, most related studies have ignored how to master arterial traffic signal control. We cannot easily extract useful information and search solution space because the arterial traffic control problem has large state-action spaces. Here we tackle these issues by proposing a multi-agent attention-base soft actor-critic (MASAC) model to master arterial traffic control. Specifically, we implement the attention mechanism in the actor and critic network to enhance traffic information extraction ability. More importantly, we are the first to apply the soft actor-critic (SAC) algorithm to train the arterial traffic control model to search more solution spaces. Testing results indicate that the MASAC method significantly outperforms existing MARL algorithms and the multiband-based method. These findings can help researchers to design better model structures for other MARL problems.