Evolutionary Algorithm Framework Research Articles

Economically optimized heat exchanger design: a synergistic approach using differential evolution and equilibrium optimizer within an evolutionary algorithm framework

Economically optimized heat exchanger design: a synergistic approach using differential evolution and equilibrium optimizer within an evolutionary algorithm framework

Compressor airfoil optimization method driven by data-mechanism integration based on evolutionary multi-tasking algorithm

To address the challenge of the "curse of dimensionality" in aerodynamic design optimization of compressors, this study introduces an innovative optimization technique suitable for compressor airfoil design. This technique, rooted in a hybrid mechanism-data-driven approach, seamlessly integrates a hierarchical parameterization method, based on elliptic topological deformation, into a multitasking evolutionary algorithm framework. This integration deviates from the conventional approach of treating parameterization methods and optimization algorithms as distinct elements. The proposed method positions airfoil parameterization as its core, constructing two tasks within the optimization algorithm. It leverages the critical influence of the parameterization method on the aerodynamic performance landscape of the airfoils and the intrinsic qualities of the hierarchical parameterization method in the design space. The multitasking evolutionary optimization framework facilitates effective information exchange between tasks, significantly boosting optimization efficiency. In comparison to standard data-driven multitasking evolutionary algorithms, the proposed method achieves superior optimized solutions with merely 11 × D aerodynamic performance evaluations, where D denotes the number of design variables.

Reinforcement learning-assisted evolutionary algorithm: A survey and research opportunities

Evolutionary algorithms (EA), a class of stochastic search methods based on the principles of natural evolution, have received widespread acclaim for their exceptional performance in various real-world optimization problems. While researchers worldwide have proposed a wide variety of EAs, certain limitations remain, such as slow convergence speed and poor generalization capabilities. Consequently, numerous scholars actively explore improvements to algorithmic structures, operators, search patterns, etc., to enhance their optimization performance. Reinforcement learning (RL) integrated as a component in the EA framework has demonstrated superior performance in recent years. This paper presents a comprehensive survey on integrating reinforcement learning into the evolutionary algorithm, referred to as reinforcement learning-assisted evolutionary algorithm (RL-EA). We begin with the conceptual outlines of reinforcement learning and the evolutionary algorithm. We then provide a taxonomy of RL-EA. Subsequently, we discuss the RL-EA integration method, the RL-assisted strategy adopted by RL-EA, and its applications according to the existing literature. The RL-assisted procedure is divided according to the implemented functions including solution generation, learnable objective function, algorithm/operator/sub-population selection, parameter adaptation, and other strategies. Additionally, different attribute settings of RL in RL-EA are discussed. In the applications of RL-EA section, we also demonstrate the excellent performance of RL-EA on several benchmarks and a range of public datasets to facilitate a quick comparative study. Finally, we analyze potential directions for future research. This survey serves as a rich resource for researchers interested in RL-EA as it overviews the current state-of-the-art and highlights the associated challenges. By leveraging this survey, readers can swiftly gain insights into RL-EA to develop efficient algorithms, thereby fostering further advancements in this emerging field.

An Interactive Knowledge-Based Multiobjective Evolutionary Algorithm Framework for Practical Optimization Problems

Experienced users often have useful knowledge and intuition in solving real-world optimization problems. User knowledge can be formulated as inter-variable relationships to assist an optimization algorithm in finding good solutions faster. Such inter-variable interactions can also be automatically learned from high-performing solutions discovered at intermediate iterations in an optimization run – a process called innovization. These relations, if vetted by the users, can be enforced among newly generated solutions to steer the optimization algorithm towards practically promising regions in the search space. Challenges arise for large-scale problems where the number of such variable relationships may be high. This paper proposes an interactive knowledge-based evolutionary multi-objective optimization () framework that extracts hidden variable-wise relationships as knowledge from evolving high-performing solutions, shares them with users to receive feedback, and applies them back to the optimization process to improve its effectiveness. The knowledge extraction process uses a systematic and elegant graph analysis method which scales well with number of variables. The working of the proposed is demonstrated on three large-scale real-world engineering design problems. The simplicity and elegance of the proposed knowledge extraction process and achievement of high-performing solutions quickly indicate the power of the proposed framework. The results presented should motivate further such interaction-based optimization studies for their routine use in practice.

Interaction-Based Prediction for Dynamic Multiobjective Optimization

Dynamic multiobjective optimization poses great challenges to evolutionary algorithms due to the change of optimal solutions or Pareto front with time. Learning-based methods are popular to extract the changing pattern of optimal solutions for predicting new solutions. They tend to use all variables as features (i.e., inputs) to build prediction models. However, there are usually some irrelevant and redundant variables, which increase training difficulty and decrease prediction accuracy. This paper proposes a new interaction-based prediction method, which captures the correlation of variables with prediction targets and selects the most relevant variables to build prediction models using neural networks. In particular, the interaction between variables is detected to remove redundant variables. In addition, a correction procedure is developed to further improve predicted solutions according to the prediction error in past environments. The predicted solutions are used to update the population according to a specifically designed update strategy. Integrating the interaction-based prediction (IP) method into the framework of multiobjective evolutionary algorithm based on decomposition (MOEA/D), a new algorithm named IP-DMOEA is put forward. Experimental results on a typical dynamic multiobjective test suite demonstrate the better performance of the proposed IPDMOEA than state-of-the-art algorithms in terms of convergence speed and solution quality. The proposed IP-DMOEA is also successfully applied to the multirobot task scheduling problem.

Dual-Fuzzy-Classifier-Based Evolutionary Algorithm for Expensive Multiobjective Optimization

Multiobjective evolutionary algorithms (MOEAs) have been widely used to solve multiobjective optimization problems (MOPs). Conventional MOEAs usually require a large number of function evaluations (FEs) for evaluating the quality of solutions. However, only a limited number of FEs are affordable in many real-world optimization problems where the FEs are computationally or economically expensive. The use of a large number of FEs decreases the effectiveness of MOEAs in solving these problems. This article proposes a dual fuzzy classifier-based surrogate model (DFC) and a DFC-based multiobjective evolutionary algorithm framework (DFC-MOEA) for expensive optimization problems. The DFC model is used for offspring pre-selection to choose high-quality offspring solutions and for categorization to divide unevaluated solutions into three categories. In the proposed framework, two fuzzy classifiers are built to predict the quality of each unevaluated solution. To strike a balance between convergence and diversity, one classifier is designed for predicting the dominance relation of unevaluated solutions (i.e., convergence), and another classifier is designed for predicting the crowdedness of unevaluated solutions (i.e., diversity). The integration of the proposed framework in three different types of MOEAs (i.e., a dominance-based MOEA, an indicator-based MOEA, and a decomposition-based MOEA) demonstrates its usefulness in handling expensive optimization problems. Comprehensive experiments on four well-known test suites and several real-world optimization problems demonstrate that the proposed framework is able to improve the performance of MOEAs under a limited number of FEs.

A comparative study of evolutionary approaches to the bi-objective dynamic Travelling Thief Problem

Dynamic evolutionary multi-objective optimization is a thriving research area. Recent contributions span the development of specialized algorithms and the construction of challenging benchmark problems. Here, we continue these research directions through the development and analysis of a new bi-objective problem, the dynamic Travelling Thief Problem (TTP), including three modes of dynamic change: city locations, item profit values, and item availability. The interconnected problem components embedded in the dynamic problem dictate that the effective tracking of good trade-off solutions that satisfy both objectives throughout dynamic events is non-trivial. Consequently, we examine the relative contribution to the non-dominated set from a variety of population seeding strategies, including exact solvers and greedy algorithms for the knapsack and tour components, and random techniques. We introduce this responsive seeding extension within an evolutionary algorithm framework. The efficacy of alternative seeding mechanisms is evaluated across a range of exemplary problem instances using ranking-based and quantitative statistical comparisons, which combines performance measurements taken throughout the optimization. Our detailed experiments show that the different dynamic TTP instances present varying difficulty to the seeding methods tested. We posit the dynamic TTP as a suitable benchmark capable of generating problem instances with different controllable characteristics aligning with many real-world problems.

An Evolutionary-Based Mutation With Functional Annotation to Identify Protein Complexes Within PPI Networks

The research deals with an evolutionary-based mutation with functional annotation to identify protein complexes within PPI networks. An important field of research in computational biology is the difficult and fundamental challenge of revealing complexes in protein interaction networks. The complex detection models that have been developed to tackle challenges are mostly dependent on topological properties and rarely use the biological properties of PPI networks. This research aims to push the evolutionary algorithm to its maximum by employing gene ontology (GO) to communicate across proteins based on biological information similarity for direct genes. The outcomes show that the suggested method can be utilized to improve the predictability of the complexes identified. The GO functional annotation of proteins as a heuristic guide is injected into the framework of single-objective evolutionary algorithms (EAs), while the complex detection community score (CS) model works as a fitness function in EAs. In the experiments, we analyzed the performance of our proposed algorithm when applied to the publicly accessible yeast protein networks. The results show a considerable improvement in the detection ability of complexes in the PPI network.

Duel-based neuroevolutionary method for Stackelberg Security Games with boundedly rational Attacker

This paper considers the problem of finding optimal strategies in Stackelberg Security Games when playing against a non-perfectly rational Attacker. To this end, a novel Duel-based NeuroEvolutionary approach to Security Games (DNESG) is proposed, which utilizes the Strategy Comparison Neural Network (SCNN) as a surrogate model to compare pairs of Defender’s strategies. SCNN is trained on historical data (past attack attempts) and does not require any direct information about the Attacker’s preferences regarding targets, payoff distribution, or decision-making model. SCNN is embedded in the Evolutionary Algorithm framework and implements a tournament-based selection method in place of a time-consuming direct strategy evaluation. The effectiveness of DNESG is assessed on a set of 90 benchmark Deep Packet Inspection games inspired by real cybersecurity scenarios. The proposed method provides high-quality solutions and outperforms state-of-the-art approaches (both exact and approximate) with statistical significance when playing against non-perfectly rational Attacker. Moreover, DNESG offers excellent time scalability, being two orders of magnitude faster than the state-of-the-art Mixed-Integer Linear Programming method.

Self-Adaptive Spherical Search With a Low-Precision Projection Matrix for Real-World Optimization.

Since the last three decades, numerous search strategies have been introduced within the framework of different evolutionary algorithms (EAs). Most of the popular search strategies operate on the hypercube (HC) search model, and search models based on other hypershapes, such as hyper-spherical (HS), are not investigated well yet. The recently developed spherical search (SS) algorithm utilizing the HS search model has been shown to perform very well for the bound-constrained and constrained optimization problems compared to several state-of-the-art algorithms. Nevertheless, the computational burdens for generating an HS locus are higher than that for an HC locus. We propose an efficient technique to construct an HS locus by approximating the orthogonal projection matrix to resolve this issue. As per our empirical experiments, this technique significantly improves the performance of the original SS with less computational effort. Moreover, to enhance SS's search capability, we put forth a self-adaptation technique for choosing the effective values of the control parameters dynamically during the optimization process. We validate the proposed algorithm's performance on a plethora of real-world and benchmark optimization problems with and without constraints. Experimental results suggest that the proposed algorithm remains better than or at least comparable to the best-known state-of-the-art algorithms on a wide spectrum of problems.

Analyzing and Modeling Network Travel Patterns During the Ukraine Invasion Using Crowd-Sourced Pervasive Traffic Data

In 2022, Ukraine is suffering an invasion which has resulted in acute impacts playing out over time and geography. This paper examines the impact of the ongoing disruption on traffic behavior using analytics as well as zonal-based network models. The methodology is a data-driven approach that utilizes obtained travel-time conditions within an evolutionary algorithm framework which infers origin–destination demand values in an automated process based on traffic assignment. Because of the automation of the implementation, numerous daily models can be approximated for multiple cities. The novelty of this paper versus the previously published core methodology includes an analysis to ensure the obtained data is appropriate, since some data sources were disabled because of the ongoing disruption. Further novelty includes a direct linkage of the analysis to the timeline of disruptions to examine the interaction in a new way. Finally, specific network metrics are identified which are particularly suited for conceptualizing the impact of conflict disruptions on traffic network conditions. The ultimate aim is to establish processes, concepts, and analysis to advance the broader activity of rapidly quantifying the traffic impacts of conflict scenarios.

Socio-inspired evolutionary algorithms: a unified framework and survey

It is well recognized that human societies and their problem-solving capabilities have evolved much faster than biological evolution. The inspiration from human behaviors, knowledge exchange, and transformation has given rise to a new evolutionary computation paradigm. Multiple research endeavors have been reported in the literature inspired by the diverse aspects of human societies with corresponding terminologies to describe the algorithm. These endeavors have resulted in piles of algorithms, worded differently but with more or less similar underlying mechanisms causing immense confusion for a new reader. This paper presents a generalized framework for these socio-inspired evolutionary algorithms (SIEAs) or socio-inspired meta-heuristic algorithms. A survey of various SIEAs is provided to highlight the working of these algorithms on a common framework, their variations and improved versions proposed in the literature, and their applications in various fields of search and optimization. The algorithmic description and the comparison of each SIEA with the general framework enable a clearer understanding of the similarities and differences between these methodologies. Efforts have been made to provide an extensive list of references with due context. This paper could become an excellent reference as a starting point for anyone interested in this fascinating field of research.

Novel multi objective evolutionary framework for solving next release problem

Software developers want to meet the requirements of customers in next versions. Choosing which set of requirements can be done according to cost and time is an NP-hard problem known as Next Release Problem (NRP). In this article, a multi objective evolutionary algorithm (MOEA) framework is proposed to solve NRP. The framework applies the non-repetitive population, integrates solutions and external repository. Furthermore, a novel approach is implemented to satisfy the constraints of the problem. In this framework, six evolutionary algorithms are implemented and using seven quality indicators, the achieved results of that algorithms are compared with the original versions of same algorithms. Through using HV (the ratio of the region covered by Pareto Front) and NDS (the number of solutions in the Pareto Front) metrics, the effects of the proposed algorithms are compared with other works’ results. The efficacy of the proposed MOEA framework is measured using three real world datasets. The gained results represent that the implemented algorithms perform better than other related algorithms previously published.

Transfer learning based evolutionary algorithm framework for multi-objective optimization problems

Transfer learning based evolutionary algorithm framework for multi-objective optimization problems

Applications and challenges of hybrid artificial intelligence in chip age testing: a comprehensive review

As technology rapidly advances, semiconductor devices play a crucial role in various fields. However, these devices experience aging over time, leading to performance degradation, failure, or system crashes. Real-time aging detection of semiconductor devices is essential. This paper presents a real-time aging detection technique for semiconductor devices, combining deep learning and evolutionary algorithms, effectively assessing and predicting device aging states using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). These features are then input into evolutionary algorithm frameworks, such as Genetic Algorithms (GA) and Genetic Algorithms (PSO), to identify and predict aging trends. The adaptation of evolutionary algorithms ensures good generalization for various semiconductor devices. Through extensive experimental data analysis, the proposed technique demonstrates excellent accuracy and real-time performance compared to traditional aging detection methods. In addition, it also monitors their operation in real-time, providing valuable support for maintenance and management personnel. The findings contribute to improving semiconductor device reliability and stability, providing a robust foundation for intelligent and automated maintenance.

A user-guided innovization-based evolutionary algorithm framework for practical multi-objective optimization problems

The knowledge of experienced users in solving real-world optimization problems can be formulated as inter-variable relationships to guide an optimization algorithm towards good solutions faster. Alternatively, such interactions can be learned algorithmically during the optimization by analysing good solutions—a process called innovization. Any common pattern extracted from good solutions can be used as a repair operator to modify candidate solutions. The key aspect is to strike a balance between the relevance of the pattern and the extent of its use in the repair operator. This article proposes a multi-objective evolutionary algorithm framework that combines problem-specific knowledge and online innovization approaches to solve two real-world large-scale multi-objective problems: a 879- and 1479-variable truss design and a 544-variable solid fuel rocket design. Four repair operators suitable for uncovering monotonic relations involving multiple decision variables are proposed. Performance variations resulting from different combinations of initial user knowledge and repair operators are also presented.

A general framework of surrogate-assisted evolutionary algorithms for solving computationally expensive constrained optimization problems

The objective and constraints of expensive constrained optimization problems (ECOPs) are often evaluated using simulations with different computational costs. However, the existing algorithms always assume that both the objective and constraints can be simultaneously obtained after one expensive simulation, i.e., the most time-consuming one among the evaluations, based on parallel computing. This will affect the optimization efficiency since it is not necessary to call all the simulations of objective and constraints for each solution. Therefore, a general framework of surrogate-assisted evolutionary algorithms (GF-SAEAs) is proposed to adaptively arrange search strategies based on actual simulation cost differences. Specifically, all constraints are classified into several constraint levels by effectively quantifying the computational cost differences of objective and constraints, and a level-by-level feasible region-driven local search strategy is designed to locate potential sub-feasible regions for each constraint level. Then three different search mechanisms are employed to explore and exploit these located regions. Additionally, an adaptive population regeneration strategy is utilized to restart the algorithm and prevent premature convergence. In summary, GF-SAEAs can maintain a good balance between feasibility and convergence. Experimental studies on benchmark problems and two real-world cases show that GF-SAEAs performs better than other state-of-the-art algorithms.

Real-time rescheduling approach of train operation for high-speed railways using problem-specific knowledge under a station blockage

This study considers the train timetable as the problem object to investigate the station blockage caused by emergencies. Based on the evolutionary algorithm framework, a real-time rescheduling approach using problem-specific knowledge is proposed. This approach ensures the safety and efficiency of high-speed rail operations and the satisfaction and comfort of passengers by minimizing the total train delay. First, a permutation encoding method is developed to reduce invalid searches in the search space based on the rescheduling strategy of reordering train departure sequences. Next, according to the train operation tracking mode, called tracking interval control, a heuristic decoding method is designed to eliminate all constraints of train operation to improve efficiency. Finally, the dispatcher's experience in adjusting the train timetable is used as the problem-specific knowledge to initialize the initial population of evolutionary computing. A heuristic population initialization method based on problem-specific knowledge is employed to speed up the algorithm convergence in the early stage and improve solution equality. Furthermore, the Beijing–Tianjin high-speed railway line is used as an example. Nine typical scenarios with different blockage durations of 20–150 min under the station blockage are installed at the Beijing South station. The strengthened elitist genetic algorithm (SEGA) and differential evolution (DE) are selected to perform the simulation using different combinations of the real-integer or permutation encoding and random or heuristic population initialization, respectively. The simulation results indicate that, compared with the real-integer encoding that cannot obtain feasible solutions, the two evolutionary algorithms can provide the rescheduling solution with a smaller total train delay in an average time of 9 s after using the permutation encoding. Besides, the results of the two evolutionary algorithms improved by the heuristic population initialization can quickly converge to a quasi-optimal solution. Finally, the SEGA in the permutation encoding with the heuristic population initialization is chosen as the optimal improved evolutionary algorithm. This improved evolutionary algorithm can provide quasi-optimal solutions in 20 s in the seven scenarios where the CPLEX cannot provide optimal solutions in 10 min.

A bi-level transformation based evolutionary algorithm framework for equality constrained optimization

A bi-level transformation based evolutionary algorithm framework for equality constrained optimization

ALeRSa-DDEA: active learning with reliability sampling-based evolutionary algorithm framework for solving offline data-driven expensive engineering optimization

ALeRSa-DDEA: active learning with reliability sampling-based evolutionary algorithm framework for solving offline data-driven expensive engineering optimization