Evolution Strategy Research Articles

Semi-rational design of nitroarene dioxygenase for catalytic ability toward 2,4-dichloronitrobenzene.

Rieske non-heme dioxygenase family enzymes play an important role in the aerobic biodegradation of nitroaromatic pollutants, but no active dioxygenases are available in nature for initial reactions in the degradation of many refractory pollutants like 2,4-dichloronitrobenzene (24DCNB). Here, we report the engineering of hotspots in 2,3-dichloronitrobenzene dioxygenase from Diaphorobacter sp. strain JS3051, achieved through molecular dynamic simulation analysis and site-directed mutagenesis, with the aim of enhancing its catalytic activity toward 24DCNB. The computationally predicted activity scores were largely consistent with the detected activities in wet experiments. Among them, the two most beneficial mutations (E204M and M248I) were obtained, and the combined mutant reached up to a 62-fold increase in activity toward 24DCNB, generating a single product, 3,5-dichlorocatechol, which is a naturally occurring compound. In silico analysis confirmed that residue 204 affected the substrate preference for meta-substituted nitroarenes, while residue 248 may influence substrate preference by interaction with residue 295. Overall, this study provides a framework for manipulating nitroarene dioxygenases using computational methods to address various nitroarene contamination problems.IMPORTANCEAs a result of human activities, various nitroaromatic pollutants continue to enter the biosphere with poor degradability, and dioxygenation is an important kickoff step to remove toxic nitro-groups and convert them into degradable products. The biodegradation of many nitroarenes has been reported over the decades; however, many others still lack corresponding enzymes to initiate their degradation. Although rieske non-heme dioxygenase family enzymes play extraordinarily important roles in the aerobic biodegradation of various nitroaromatic pollutants, prediction of their substrate specificity is difficult. This work greatly improved the catalytic activity of dioxygenase against 2,4-dichloronitrobenzene by computer-aided semi-rational design, paving a new way for the evolution strategy of nitroarene dioxygenase. This study highlights the potential for using enzyme structure-function information with computational pre-screening methods to rapidly tailor the catalytic functions of enzymes toward poorly biodegradable contaminants.

Evolutionary game analysis on collaborative logistics diffusion considering diminishing marginal utility under dynamic productivity

Reducing carbon emissions in the transportation sector is crucial for sustainable practices, with collaborative logistics diffusion playing a significant role in promoting energy-saving and emission-reduction strategies. This paper introduces an evolutionary game model to analyze collaboration between manufacturers and logistics service providers (LSPs), using the Hotelling quadratic curve model to measure the diminishing marginal utility of basic profits. Initially, a static strategy leads to a cyclic evolution without reaching the evolutionary stable strategy (ESS), reflecting competitive dynamics under static productivity conditions where neither party gains a lasting advantage, leading to periodic adjustments in strategies. The Cobb-Douglas function models a dynamic productivity strategy that regulates the extra benefits of collaborative logistics and penalties for 'free-riding.' This strategy demonstrates an inward spiral convergence towards the ESS, showing that adaptive mechanisms enable manufacturers and LSPs to align interests and achieve equilibrium. Enhanced benefits from collaborative logistics promote a stable and cooperative ecosystem, discouraging free-riding and enhancing cost efficiency. Moreover, increased investment by manufacturers not only strengthens their position but also fosters an environment conducive for LSP participation, expediting collaborative logistics diffusion and minimizing potential losses.

Dual stress factors adaptive evolution for high EPA production in Schizochytrium sp. and metabolomics mechanism analysis.

Eicosapentaenoic acid (EPA) is a vital ɷ-3 polyunsaturated fatty acid (PUFA) for human body with various physiological functions. In this study, we proposed an adaptive evolutionary strategy based on high-temperature and high-oxygen two-factor stress to increase the EPA production capacity of Schizochytrium. High-temperature stress was used to increase EPA yield, and high oxygen was implemented to continuously stimulate cell growth and lipid accumulation. The biomass and EPA production of ALE-D50 reached 35.33g/L and 1.54g/L, which were 43.85% and 71.11% higher than that of the original strain, respectively. Lower in vivo reactive oxygen species levels indicated that the evolved strain possessed stronger antioxidant activity. Liquid chromatography-mass spectrometry metabolomics showed that enhanced glucose consumption and glycolysis metabolism, as well as a weakened tricarboxylic acid cycle and reduced amino acid metabolic tributaries in the evolved strain, might be associated with increased growth and EPA synthesis. Finally, the lipid production and EPA production in a fed-batch fermentation were further increased to 48.93g/L and 3.55g/L, improving by 54.30% and 90.86%, respectively. This study provides a novel pathway for promoting EPA biosynthesis in Schizochytrium.

Which policy can effectively promote the formal recycling of power batteries in China?

China's power battery recycling (PBR) market is embryonic, facing a 'Gresham's Law’. A critical concern for the government is ascertaining policies that can effectively enhance formal PBR and boost the competitiveness of authorized channels. Our study develops an evolutionary game model incorporating the government, recyclers, and consumers, investigating the impact of subsidies for formal recyclers, consumer subsidies, and deposit refund policies on the formal recycling process. The main conclusions are as follows. (1) The evolutionary strategy for the PBR game system is (non-regulation, formal recycling, green behavior) when recyclers acquire substantial reputation gains from formal recycling, and a non-regulation approach outstrips the government's benefits from regulation. (2) Subsidies for recyclers are more effective in fostering formal recycling of power batteries than consumer subsidies. When the government subsidy to formal recyclers increases from 0 yuan/kWh to 40 yuan/kWh, The evolutionary stable state transitions from (regulation, informal recycling, non-green behavior) to (non-regulation, formal recycling, green behavior). The incentive impact of consumer subsidies is relatively negligible. (3) The deposit refund policy significantly advances formal PBR, with both the increase in deposit amounts and refund ratios capable of reversing the low rates of formal recycling.

Microbial-mediated conversion of soil organic carbon co-regulates the evolution of antibiotic resistance

The influence of organic carbon on the proliferation of antibiotic resistance genes (ARGs) in the soil has been widely documented. However, it is unclear how soil organic carbon (SOC) interacts with the evolution of antibiotic resistance in bacteria. Here, we examined the variations in ARGs abundance during SOC mineralization and explored the microbiological mechanisms and key metabolic pathways involved in their coevolution. The results showed that the SOC mineralization rate was closely correlated with ARGs abundance (p < 0.05). High organic carbon (OC) mineralization was conducive to the occurrence of multidrug resistance genes. For example, multidrug_transporter and mexB increased 2.26 and 7.83 times from the initial level. The competitor (stress) evolutionary strategy model revealed that higher OC inputs drive environmental microorganisms to evolve from stress tolerant to high resistance and strong adaptation. Meta-genomic and transcriptomic analyses revealed that the conversion process of pyruvate to acetyl-CoA to acetate was the critical metabolic pathway for the co-regulation of antibiotic resistance. Gene deletion validation trials have demonstrated that the key functional genes (ackA and pta) involved in this process can modulate the development of vancomycin and multidrug resistance. This outcome provides a preliminary framework for microbial mechanisms that target the co-regulation of microbial OC conversion and the evolution of antibiotic resistance.

Research on evolutionary game of digital twin data information sharing based on blockchain technology

Due to the significant characteristics of decentralization and tamper resistance of blockchain technology, it helps to solve the problems of timeliness and slow permission control in traditional information sharing. However, most researchers are committed to exploring how to improve the efficiency, reliability, and privacy security of digital twin data. There is a lack of research on issues such as blockchain participants free-riding and not actively participating in information sharing, and how to promote information sharing through incentives. Therefore, this paper designs a distance grouping based practical byzantine fault tolerance consensus algorithm to address the credibility issue of transmitted data, thereby improving the efficiency of negotiation. The feasibility of the algorithm is verified through cases analysis. In addition, for the problem of low subjective willingness of information sharing, this paper designs an information sharing incentive strategy based on blockchain intelligent contracts, establishes digital twin data sharing evolutionary game models with reputation incentives and non-reputation incentives, so as to explore the impact of reputation incentive on evolutionary stability strategies. Finally, simulation analysis is conducted on the evolutionary game model of digital twin data sharing. The impact of several key indicators such as data complementarity, trust, positive incentive coefficient, and data sharing cost on the evolution process of information sharing are considered, and corresponding management insights are obtained.

Adult Human, but Not Rodent, Spermatogonial Stem Cells Retain States with a Foetal-like Signature

Spermatogenesis involves a complex process of cellular differentiation maintained by spermatogonial stem cells (SSCs). Being critical to male reproduction, it is generally assumed that spermatogenesis starts and ends in equivalent transcriptional states in related species. Based on single-cell gene expression profiling, it has been proposed that undifferentiated human spermatogonia can be subclassified into four heterogenous subtypes, termed states 0, 0A, 0B, and 1. To increase the resolution of the undifferentiated compartment and trace the origin of the spermatogenic trajectory, we re-analysed the single-cell (sc) RNA-sequencing libraries of 34 post-pubescent human testes to generate an integrated atlas of germ cell differentiation. We then used this atlas to perform comparative analyses of the putative SSC transcriptome both across human development (using 28 foetal and pre-pubertal scRNA-seq libraries) and across species (including data from sheep, pig, buffalo, rhesus and cynomolgus macaque, rat, and mouse). Alongside its detailed characterisation, we show that the transcriptional heterogeneity of the undifferentiated spermatogonial cell compartment varies not only between species but across development. Our findings associate ‘state 0B’ with a suppressive transcriptomic programme that, in adult humans, acts to functionally oppose proliferation and maintain cells in a ready-to-react state. Consistent with this conclusion, we show that human foetal germ cells—which are mitotically arrested—can be characterised solely as state 0B. While germ cells with a state 0B signature are also present in foetal mice (and are likely conserved at this stage throughout mammals), they are not maintained into adulthood. We conjecture that in rodents, the foetal-like state 0B differentiates at birth into the renewing SSC population, whereas in humans it is maintained as a reserve population, supporting testicular homeostasis over a longer reproductive lifespan while reducing mutagenic load. Together, these results suggest that SSCs adopt differing evolutionary strategies across species to ensure fertility and genome integrity over vastly differing life histories and reproductive timeframes.

Zooplankton seasonal vertical migration in an optimality-based plankton ecosystem model

Abstract Several species from various zooplankton taxa perform seasonal vertical migrations (SVM) of typically several hundred meters between the surface layer and overwintering depths, particularly in high-latitude regions. We use OPtimality-based PLAnkton (OPPLA) ecosystem model) to simulate SVM behavior in zooplankton in the Labrador Sea. Zooplankton in OPPLA is a generic functional group without life cycle, which facilitates analyzing SVM evolutionary stability and interactions between SVM and the plankton ecosystem. A sensitivity analysis of SVM-related parameters reveals that SVM can amplify the seasonal variations of phytoplankton and zooplankton and enhance the reduction of summer surface nutrient concentrations. SVM is often explained as a strategy to reduce exposure to visual predators during winter. We find that species doing SVM can persist and even dominate the summer-time zooplankton community, even in the presence of Stayers, which have the same traits as the migrators, but do not perform SVM. The advantage of SVM depends strongly on the timing of the seasonal migrations, particularly the day of ascent. The presence of higher (visual) predators tends to suppress the Stayers in our simulations, whereas the SVM strategy can persist in the presence of non-migrating species even without higher predators.

Chaotic opposition Golden Sinus Algorithm for global optimization problems

Optimization techniques are required to find the best solutions to challenging problems in many engineering disciplines. Metaheuristic algorithms that effectively increase search performance using various evolutionary strategies have become increasingly popular in recent years. The Golden Sinus Algorithm (GoldSa) is a population-based optimization algorithm that uses the sine function and the golden ratio. In this study, a chaotically enhanced opposition-based Golden Sinus Algorithm (Co-GoldSa) has been proposed to improve the efficiency of the exploitation and exploration ability of the GoldSa method. While designing this approach, it is first necessary to analyze the chaotic behavior of the GoldSa parameters. For this purpose, three chaotic GoldSa methods have been developed using eight chaotic maps to determine the effect of the chaotic maps on the parameters with different behaviors. Secondly, the opposition-based learning strategy is adjusted to the cGoldSa to enhance the searching ability. To investigate the proficiency of the proposed Co-GoldSa method, it has been examined with well-known and newly introduced metaheuristic approaches on benchmark functions and classical engineering design problems. Besides, an efficient framework of the multilevel thresholding image segmentation has been presented based on the Co-GoldSa method since the efficient processing of pathological images is quite important in medical diagnostics. The experimental outcomes reveal the superiority of the proposed method in solving global optimization problems, image segmentation, and engineering problems. Thus, the outcomes of the benchmark functions, image segmentation, and classical engineering problems support that the proposed Co-GoldSa approach can be considered a promising method for resolving challenging optimization problems.

Distribution Network Reconfiguration Based on an Improved Arithmetic Optimization Algorithm

Aiming to address the defects of the arithmetic optimization algorithm (AOA), such as easy fall into local optimums and slow convergence speed during the search process, an improved arithmetic optimization algorithm (IAOA) is proposed and applied to the study of distribution network reconfiguration. Firstly, a reconfiguration model is established to reduce network loss, and a cosine control factor is introduced to reconfigure the math optimization accelerated (MOA) function to coordinate the algorithm’s global exploration and local exploitation capabilities. Subsequently, a reverse differential evolution strategy is introduced to improve the overall diversity of the population and Weibull mutation is performed on the better-adapted individuals generated in each iteration to ensure the quality of the optimal individuals generated in each iteration and strengthen the algorithm’s ability to approach the optimal solution. The performance of the improved algorithm is also tested using eight basis functions. Finally, simulation analysis is carried out by taking the IEEE33 and IEEE69 node systems and a real power distribution system as examples; the results show that the proposed algorithm can help to reconfigure the system quickly, and the system node voltages and network losses were significantly improved after the reconfiguration.

Evolution Process of Urban Industrial Land Redevelopment in China: A Perspective of Original Land Users

The crucial role of urban industrial land redevelopment in sustainable urban renewal has garnered widespread attention. While some scholars have explored the interest game among stakeholders in industrial land redevelopment, they primarily focus on the government-led model. Moreover, there remains a research gap concerning the impact of government intervention on the redevelopment of industrial land. This article utilizes evolutionary game theory to investigate the interest game between local governments and original land users in the model of urban industrial land redevelopment dominated by original land users. We establish evolutionary game models considering incentives and the combination of incentives and regulations, explore the interest balance strategy, and examine the impact of positive incentives and mandatory regulations on industrial land redevelopment. Furthermore, we employ a numerical simulation to unveil the impact of initial strategies and parameter adjustments on game strategy. The research results are as follows: (1) Under the original land user-led redevelopment model, only two evolutionary stability strategies exist: either the original land users implement industrial land redevelopment with positive responses from local governments, or neither party advances the process. (2) Government intervention is pivotal in facilitating the redevelopment of inefficient industrial land as economic subsidies and punitive measures motivate more participants to adopt proactive strategies. (3) The increase in government support positively correlates with the likelihood of industrial land redevelopment implementation by original land users. (4) The interests and costs of original land users emerge as crucial parameters influencing strategic decisions. This study enriches the understanding of the interests of core participants in industrial land redevelopment and provides valuable insights for sustainable urban renewal.

A distributed evolutionary based instance selection algorithm for big data using Apache Spark

Instance selection is an important preprocessing technology in data mining and machine learning. In this paper, we proposed a novel evolutionary based instance selection algorithm for big data. First, we defined a coarse granularity chromosome structure to reduce the size of search space and costs of chromosome operations(recombination and mutation, etc.). Then a stratified evolution strategy was proposed to remove the hyper parameter in classic fitness function and achieve precise control over the reduction ratio of instances. Finally, a sampling-based fitness function was proposed to reduce the time complexity. Experimental results shown that our new algorithm is efficient to complete the instance selection task on data set with millions of instances in minutes-level. The 10-fold cross-validation also proved that the selection results on many datasets have high nearest neighbor classification accuracy.

Evolutionary multitasking for multiobjective optimization based on hybrid differential evolution and multiple search strategy

Multitask optimization uses the knowledge transfer between tasks to deal with multiple related tasks simultaneously, which obtains better optimization performance. Currently, most multitask optimization algorithms have shortings such as slow convergence speed, difficulty in generating high-quality solutions and insufficient population diversity. To solve these problems, an new multiobjective multitask evolutionary algorithm is proposed in this paper. The algorithm introduces hybrid differential evolution strategy and multiple search strategy to generate offspring and high-quality solutions. In order to accelerate the population convergence and improve the lack of population diversity, we mixed two differential mutation operators with different functions to generate offspring. While generating high-quality solutions to improve the population convergence speed, some random solutions can also be generated to maintain the population diversity, which enhances the ability of the algorithm to jump out of the local optimal. Secondly, the multiple search strategy uses the information collected from multiple dimensions to optimize decision variables, and generates high-quality solutions by migrating useful knowledge. Finally, the performance of the algorithm is verified on two classical multitask test set. Compared with other related algorithms, the proposed algorithm has a faster convergence speed and better distribution performance, and shows a good ability to jump out of local optimal.

Monkeypox virus genomic accordion strategies.

The 2023 monkeypox (mpox) epidemic was caused by a subclade IIb descendant of a monkeypox virus (MPXV) lineage traced back to Nigeria in 1971. Person-to-person transmission appears higher than for clade I or subclade IIa MPXV, possibly caused by genomic changes in subclade IIb MPXV. Key genomic changes could occur in the genome's low-complexity regions (LCRs), which are challenging to sequence and are often dismissed as uninformative. Here, using a combination of highly sensitive techniques, we determine a high-quality MPXV genome sequence of a representative of the current epidemic with LCRs resolved at unprecedented accuracy. This reveals significant variation in short tandem repeats within LCRs. We demonstrate that LCR entropy in the MPXV genome is significantly higher than that of single-nucleotide polymorphisms (SNPs) and that LCRs are not randomly distributed. In silico analyses indicate that expression, translation, stability, or function of MPXV orthologous poxvirus genes (OPGs), including OPG153, OPG204, and OPG208, could be affected in a manner consistent with the established "genomic accordion" evolutionary strategies of orthopoxviruses. We posit that genomic studies focusing on phenotypic MPXV differences should consider LCR variability.

Residential building energy consumption estimation: A novel ensemble and hybrid machine learning approach

In recent decades, there has been a substantial rise in both worldwide energy consumption and the accompanying increase in Carbon Dioxide (CO2) emissions, primarily propelled by population growth and the escalating demand for personal comfort. Operational energy consumption in buildings constitutes about 30% of the world's total final energy usage, underscoring the significance of predicting building energy usage for effective energy planning, management, and conservation. This study has enhanced the prediction of heating load (HL) and cooling loads (CL) for residential buildings using novel and dependable machine learning (ML) techniques. The study utilized two base models: Adaptive Boosting (ADA) and Extreme Gradient Boosting (XGBoost). An ensemble consisting of ADA and XGB was constructed to improve the model's performance, aligning with the principles of the Dempster-Shafer theory. To optimize the efficiency of ADA and XGB, five innovative optimizers, namely Victoria Amazonica Optimization (VAO), Giant Trevally Optimizer (GTO), Covariance Matrix Adaptation Evolution Strategy (CMAES), Coyote Optimization Algorithm (COA), and Mountain Gazelle Optimizer (MGO), were integrated. Statistical analysis has been employed to evaluate the performance of the proposed models. The results highlight the effectiveness of CMAES in optimizing the XGB and ADA models for predicting HL and CL. The most accurate result was achieved by the XGCM hybrid model, as evidenced by the impressive total R2 value of 0.9934 in HL and 0.9911 in CL prediction. The experimental findings illustrate that the suggested approach exhibits superior predictive performance across various scenarios of building energy consumption.

A new evolutionary strategy for reinforcement learning

A new evolutionary strategy for reinforcement learning

Decision analysis of PPP project's parties based on deep consumer participation.

Although PPP(Public-private partnership) mode has been applied for a long time in infrastructural project, the success rate is not very high. The sustainability of PPP projects is still influenced by many factors. In order to examine the evolutionary stable strategies (ESSs) of social capital, government, and paying consumers, a tripartite evolutionary game model is established in this work. In order to further promote consumer participation, it is necessary to make the assumption that customer oversight and review can have an impact on service prices. The results show: i)The strategy choice of consumer depends on the comparison between supervision cost of consumer and price coefficient for consumer to social capital. ii)Consumer supervision can promote the provision of high-quality services by social capital. iii)The difference between high-quality cost and low-quality cost, subsidy coefficient, price coefficient and supervision cost of consumer are critical factors influencing both evolutionary results and trajectories. This paper also puts forward policy implications for the three stakeholders to promote social capital's high-quality strategy so as to maintain the sustainability of PPP projects.

Analysis of Digital Security Governance under the Objectives of Digital Ecology: A Three-Party Evolutionary Game Approach

With the rapid development of the digital economy, there has been an explosion in the amount of data generated. Data have become a vital resource for nations, just as tangible assets and human capital are crucial factors of production. Consequently, protecting digital security has become paramount. However, the increasing frequency of various data security incidents in recent years has exposed issues such as inadequate platform governance, lack of government regulation, and incomplete digital security governance (DSG) mechanisms. This study aims to create a healthy and open digital ecosystem and proposes a new digital security governance framework. It subdivides traditional government departments into local governments and the central government. Together with third-party platforms, they are analyzed as participants in an evolutionary game. The study examines the evolutionary stability of strategy choices made by each party and explores the relationship between key factors such as negative externalities, data security incident probabilities, and their impacts on strategy selection using numerical simulations. The research findings indicate the following. (1) Key parameters such as implicit benefits, government subsidies, and negative externalities play a significantly positive role in the development of the digital ecosystem. (2) The central government consistently tends towards emergency response, considering the overall societal perspective, as long as it is capable of bearing the costs. (3) Local governments may exhibit free-riding behavior during the governance process. To address this, it is important to increase the willingness of third-party platforms to govern and oversee the participation of local governments, which is an effective way to prevent data crises. The study also identifies different governance models in various environments: (1) a digital security governance model in a stable market environment, involving increased central government intervention and supervision of local governments’ participation; and (2) a digital security governance model in the event of a data crisis, where the central government establishes subsidies that exceed the governance costs of local governments to enhance the willingness of third-party platforms to govern. Finally, recommendations and strategies are presented to enhance the level of digital security governance.

Extending Grime's CSR model to predict plant demographic responses across resource availability gradients: evidence from the patagonian steppes

Sexual reproduction, growth, and survival are crucial demographic strategies for plant population viability. Here, we propose a conceptual model predicting demographic responses of species based on their ecological strategy and the heterogeneity of environmental conditions within a biogeographical unit and then applied it to a case study from a 5˚ latitudinal gradient in the Patagonian steppes. We also aim to disentangle genetic from environmental effects on demographic responses. We performed in situ and common garden experiments with two species from six local populations of the Occidental Phytogeographical District of the Patagonian steppes. Species differ in key ecological traits, and thus fit into Grime's model for evolutionary strategies in plants: one as competitive species and the other as stress‐tolerant species. We calculated population growth rate (λ) and performed elasticity analyses to compare the contribution of each demographic strategy to population fitness between species and among local populations distributed along 600 km latitudinal gradient with differences in mean annual precipitation (MAP). We highlight four results. First, the competitive species change from sexual reproduction to growth as MAP increases. Second, the stress‐tolerant species relied on growth and survival along the MAP gradient. Third, interannual variation in resource availability modulated demographic responses for both strategies. Fourth, based on the comparison of the in situ and common garden experiments, we submit that demographic responses were genetically driven. Our study shows that demographic responses can be roughly predicted by the ecological strategy across environmental gradients. We show that differences arise not only between species, but also were genetically driven differences within species among local populations. Scaling up plant‐level responses to population‐level dynamics allows for a process‐based understanding of current and future biogeographical species organization. Furthermore, conservation and restoration efforts should be guided by demographic strategies underlying population viability.

Efficient Sizing and Layout Optimization of Truss Benchmark Structures Using ISRES Algorithm

This paper presents a comprehensive investigation into the application of the Improved Stochastic Ranking Evolution Strategy (ISRES) algorithm for the sizing and layout optimization of truss benchmark structures. Truss structures play a crucial role in engineering and architecture, and optimizing their designs can lead to more efficient and cost-effective solutions. The ISRES algorithm, known for its effectiveness in multi-objective optimization, is adapted for the single-objective optimization of truss designs with multiple design constraints. This study encompasses a wide range of truss benchmark structures, including 10-bar, 15-bar, 18-bar, 25-bar, and 72-bar configurations, each subjected to distinct loading conditions and stress constraints. The objective is to minimize the truss weight while ensuring stress and displacement limits are met. Through extensive experimentation, the ISRES algorithm demonstrates its ability to efficiently explore the solution space and converge to optimal solutions for each truss benchmark structure. The algorithm effectively handles the complexity of the problems, which involve numerous design variables, stress constraints, and nodal displacement limits. A comparative analysis is conducted to assess the performance of the ISRES algorithm against other state-of-the-art optimization methods reported in the literature. The comparison evaluates the quality of the solutions and the computational efficiency of each method. Furthermore, the optimized truss designs are subjected to finite element analysis to validate their structural integrity and stability. The verification process confirms that the designs adhere to the imposed constraints, ensuring the safety and reliability of the final truss configurations. The results of this study demonstrate the efficacy of the ISRES algorithm in providing practical and reliable solutions for the sizing and layout optimization of truss benchmark structures. The algorithm’s competitive performance and robustness make it a valuable tool for structural engineers and designers, offering a versatile and powerful approach for complex engineering optimization tasks. Overall, the findings contribute to the advancement of optimization techniques in structural engineering, promoting the development of more efficient and cost-effective truss designs for a wide range of engineering and architectural applications. The study’s insights empower practitioners to make informed decisions in selecting appropriate optimization strategies for complex truss-design scenarios, fostering advancements in structural engineering and sustainable design practices.