Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model

The present study proposes a multipurpose reservoir operation optimization for mitigating impact of rice fields’ contamination on the downstream river ecosystem. The developed model was applied in the Tajan River basin in Mazandaran Province, Iran, in which the rice is the main crop. We used soil and water assessment tool (SWAT) to simulate inflow of the reservoir and nitrate load at downstream river reach. Nash–Sutcliffe model efficiency coefficient was used to measure the robustness of SWAT. NSE indicated that SWAT is acceptable to simulate nitrate load of the rice fields. The results of SWAT was applied in the structure of a multipurpose reservoir operation optimization in which three metaheuristic algorithms including differential evolution algorithm, particle swarm optimization and biogeography-based algorithm were utilized in the optimization process. Reliability index, mean absolute error and failure index were used to measure the robustness of the optimization algorithms. Fuzzy Technique for Order of Preference by Similarity to Ideal Solution was utilized to select the best algorithm. Based on results, particle swarm optimization is the best method to optimize reservoir operation in the case study. The reliability index and mean absolute error for water supply are 0.6 and 5 million cubic meters, respectively. Furthermore, the failure index of contamination is 0.027. Hence, it could be concluded that the proposed optimization system is reliable and robust to mitigate losses and nitrate contamination simultaneously. However, its performance is not perfect for minimizing impact of contamination in all the simulated months.

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.

Influence of initialization on the performance of metaheuristic optimizers

Comprehensive environmental models such as the Soil and Water Assessment Tool (SWAT) are becoming an integral part of decision making processes for effective planning and management of natural resources. Before their use as decision making aid, however, models must be properly evaluated to improve their prediction accuracy and reduce the likelihood of making decisions that could lead to undesirable policy outcomes. Model evaluation refers to practices such as quality analysis of input data, sensitivity analysis, calibration and verification, and uncertainty analysis. Many methodologies have been developed for model evaluations over the years. One of the major limitations of the existing model evaluation methods, in particular model calibration methods, is their computational inefficiency, especially when used to calibrate comprehensive watershed simulation models. It may take weeks to months of CPU time, depending on the problem size, to successfully calibrate a comprehensive watershed simulation model on a standard PC. In this study, two sensitivity analysis methods and four calibration methods are used to evaluate SWAT to improve its streamflow prediction accuracy for the Morro Bay watershed located on the central coast of California. Parameter sensitivity analysis was performed using step-wise-regression analysis and the one-factor-at-a time screening method. Calibration was performed using PEST, Genetic Algorithms, the Shuffled Complex Evolution, and the Dynamically Dimensioned Search using observed data from multiple sites in the watershed. The model evaluation methods are compared in terms of their computational efficiency as well as effectiveness to determine “accurate” results. The developed SWAT model can be used to evaluate effectiveness of the Best Management Practices installed in the Morro Bay watershed, and to also prioritize sites where BMPs may be implemented in the future to further improve ecological integrity of the Morro Bay Estuary, which is one of the most important wetlands in California as it supports wide variety of habitats including numerous sensitive and endangered plant and animal species.

Land Use/Land Cover (LU/LC) change is among the dominant driving factors that directly influence water balance by transforming hydrological responses. Consequently, a thorough comprehension of its impacts is imperative for sustainable water resource planning and development, notably in developing worlds such as Pakistan, where agriculture is a major livelihood. This research intends to assess the continuing changes in LU/LC and evaluate their probable repercussions on the hydrological regime of the Potohar Plateau. The maximum likelihood classification (MLC) algorithm for land use classification of the high-resolution satellite imageries, the Cellular-Automata Markov Chain Model (CA-MCM) for the projection of LU/LC maps, and the Soil and Water Assessment Tool (SWAT) in tandem with SWAT-CUP for hydrological modeling were employed in this research. The high-resolution climatic dataset (10 × 10 km) was used in SWAT. The LU/LC analysis revealed a continual propagation of agricultural and built-up lands at the detriment of forest and barren land during the last three decades, which is anticipated to continue in the future, too. Hydrological model calibrations and validations were performed on multi-basins, and the performance was evaluated using different statistical coefficients, e.g., the coefficient of determination (R2), Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and Percent Bias (PBIAS). The results yielded that the model performed very well and demonstrated the model’s robustness in reproducing the flow regime. The water balance study revealed that the anticipated LU/LC changes are projected to decrease the mean annual surface runoff, water yield, and streamflow due to an increase in percolation, lateral flow, sub-surface flow, and evapotranspiration. More significant variations of the water balance components were observed at the sub-basin level, owing to the heterogeneous spatial distribution of LU/LC, than at the basin level. The outcome of this study will provide pragmatic details to legislative bodies to develop land and water management ameliorative strategies to harness hydrological changes sustainably.

PurposeA set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose.Design/methodology/approachIn the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC.FindingsThe proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm.Originality/valueAs yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

This article proposes the hybrid Nelder–Mead (NM)–Particle Swarm Optimization (PSO) algorithm based on the NM simplex search method and PSO for the optimization of multimodal functions. The hybrid NM–PSO algorithm is very easy to implement, in practice, since it does not require gradient computation. This hybrid procedure performed the exploration with PSO and the exploitation with the NM simplex search method. In a suite of 17 multi-optima test functions taken from the literature, the computational results via various experimental studies showed that the hybrid NM–PSO approach is superior to the two original search techniques (i.e. NM and PSO) in terms of solution quality and convergence rate. In addition, the presented algorithm is also compared with eight other published methods, such as hybrid genetic algorithm (GA), continuous GA, simulated annealing (SA), and tabu search (TS) by means of a smaller set of test functions. On the whole, the new algorithm is demonstrated to be extremely effective and efficient at locating best-practice optimal solutions for multimodal functions.

The performances of Particle Swarm Optimization and Genetic Algorithm have been compared to develop a methodology for concurrent and integrated design of mechanical structure and controller of a 2-dof robotic manipulator solving tracking problems. The proposed design scheme optimizes various parameters belonging to different domains (that is, link geometry, mass distribution, moment of inertia, control gains) concurrently to design manipulator, which can track some given paths accurately with a minimum power consumption. The main strength of this study lies with the design of an integrated scheme to solve the above problem. Both real-coded Genetic Algorithm and Particle Swarm Optimization are used to solve this complex optimization problem. Four approaches have been developed and their performances are compared. Particle Swarm Optimization is found to perform better than the Genetic Algorithm, as the former carries out both global and local searches simultaneously, whereas the latter concentrates mainly on the global search. Controllers with adaptive gain values have shown better performance compared to the conventional ones, as expected.

Recently, focus on maintenance strategies has been shifted towards prognostic health management (PHM) and a number of state of the art algorithms based on data-driven prognostics have been developed to predict the health states of degrading components based on sensory data. Amongst these algorithms, Multiclass Support Vector Machines (MC-SVM) has gained popularity due to its relatively high classification accuracy, ability to classify multiple patterns and capability to handle noisy / incomplete data. However, its application is limited by the difficulty in determining the required kernel function and penalty parameters. To address this problem, this paper proposes a hybrid differential evolution – particle swarm optimization (DE-PSO) algorithm to optimize the MC-SVM kernel function and penalty parameters. The differential algorithm (DE) obtains the search limit for the SVM parameters, while the particle swarm optimization algorithm (PSO) determines the global optimum parameters for a given training data set. Since degrading machinery components display several degradation stages in their lifetime, the MC-SVM trained with optimum parameters are used to estimate the health states of a degrading machinery component, from which the remaining useful life (RUL) is predicted. This method improves the classification accuracy of MC-SVM in predicting the health states of a machinery component and consequently increases the accuracy of RUL predictions. The feasibility of the method is validated using bearing prognostic run-to-failure data obtained from NASA public data repository. A comparative study between MC-SVM with parameters obtained using simple grid search with n-fold cross validation and MC- SVM with DE-PSO based on prognostic performance metrics reveals that the proposed method has better performance, with all the cases considered falling within a 10 % error margin. The method also outperforms other soft computing methods proposed in literature.

Real-world optimization problems in engineering, logistics, and resource allocation are often constrained and multi-modal, posing a challenge for traditional optimization algorithms. Metaheuristic algorithms inspired by natural and artificial phenomena have shown promise, but many fail to balance exploration and exploitation effectively, especially under stringent constraints. Existing algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE) face issues in convergence speed and constraint handling, particularly in high-dimensional spaces or when constraints are dynamic or complex. We propose an Adaptive Pattern-Driven Optimization (APDO) algorithm, a novel tailor-inspired metaheuristic that mimics the adaptive decision-making process of a tailor designing garments. APDO integrates three primary operators—Pattern Selection, Fabric Adjustment, and Stitch Reinforcement—to handle constraints adaptively. The algorithm combines pattern memory (historical bests), probabilistic pattern mutation, and a constraint-domination principle to ensure feasibility and diversity. The core idea is to iteratively “cut and stitch” solutions to adapt the search process, enabling dynamic constraint satisfaction and global optimization. We benchmarked APDO against five popular methods (GA, PSO, DE, Firefly Algorithm, and Whale Optimization Algorithm) on a suite of 10 real-world constrained problems, including mechanical component design and energy scheduling tasks. APDO outperformed all baselines in terms of convergence speed, constraint satisfaction rate, and solution quality. In particular, APDO achieved an average feasibility rate of 97.6% and an improvement of 4.2–11.8% in best fitness across problems.

PSO has been proved as an effective supervised learning system in recent years, but it's not an effective method for incremental learning problems. Aiming at the incremental learning target for classification, a hybrid algorithm of Particle Swarm Optimization (PSO) and Artificial Immune System (AIS) called Immune based PSO (IPSO) is presented in this paper. IPSO inherits the incremental learning ability of AIS. In IPSO, training data is presented to the algorithm one by one, and the training proceed is a one-shot incremental algorithm. Besides, the swarm does not converge to a single solution; instead, each particle is a part of the classifier, and the whole memory population is taken as the integral classifier to the problem. Compared the results of standard PSO and IPSO in several benchmark problems from the UCI data sets, we found that IPSO achieved a better classification accuracy than standard PSO in most cases. It is also competitive with some of the algorithms most commonly used for classification.

Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant

The objective of the research work is to examine energy-efficient routing in wireless sensor networks for moving nodes by contrasting new evolutionary algorithms with the Particle Swarm Optimization (PSO) Algorithm. Comparative analysis of energy efficient routing is performed by a novel genetic algorithm where there are several samples (N = 10) and a PSO algorithm where there are several samples (N = 10) techniques with pre-test power of 80% using NS2. When routing wireless sensor networks with moving nodes, Various metrics, including delay, energy use, and packet delivery ratio, are used to compare PSO and genetic algorithms. The evolutionary algorithm outperforms the PSO algorithm in these ways in terms of packet delivery ratio, energy usage, and delay. (p < 0.05) indicates a statistically significant difference. The research's findings comprised the three metrics of delay, energy consumption and packet delivery ratio. The method shows that the novel Genetic algorithm outperforms the PSO algorithm for wireless sensor network energy-efficient routing for moving nodes by identifying a set of routes that can satisfy the delay restrictions.KeywordsNovel genetic algorithmParticle Swarm Optimization (PSO)Wireless sensor networkDelayEnergy consumptionPacket delivery ratio

The non-uniform inflow caused by the elbow inlet is one of the main reasons for the low actual operation performance of a centrifugal pump. Orthogonal experiment and GA_PSO algorithm are used to improve the head and efficiency of a centrifugal pump with an elbow inlet based on the method combining numerical simulation and prototype experiment in this paper. The effects of the design parameters, including elbow inlet radius ratio, blade inlet angle, blade number, blade wrap angle, blade outlet angle, impeller outlet diameter, blade outlet width and flow area ratio, on the pump head and efficiency are studied in the orthogonal experiment. The blade inlet angle is the major factor to match the non-uniform inflow and reduce the flow loss in the impeller inlet to contribute to enhancing the pump performance and cavitation characteristics. The particle swarm optimization (PSO) algorithm is optimized by integrating the genetic algorithm (GA), which ensures that the PSO-calculation result avoids falling into the local optimization and the global optimal solution is obtained as quickly as possible. The centrifugal pump with an elbow inlet is optimally designed by the GA_PSO algorithm. According to the performance test results, the efficiency of the optimized pump is 4.7% higher than that of the original pump.

Particle swarm optimization (PSO) algorithm has shown promising performances on various benchmark functions and engineering optimization problems. However, it is still difficult to achieve a satisfying trade-off between exploration and exploitation for all the optimization problems and different evolving stages. Furthermore, control parameters of some related mechanisms need pre-experience by the requirement of trial-and-error scheme. This paper presents a novel PSO algorithm, which adaptively adopts various search strategies, called Self-adapting Hybrid Strategy PSO (SaHSPS). Unlike some other peer PSO variants, this method dynamically changes the probabilities of different strategies according to their previous successful searching memories, without any additional control parameters. The probabilities of different strategies would be re-initialized according to a proposed dynamic probabilistic model to diverse the population. Besides, particles are updated by probabilistically selected strategies after niching PSO with Ring topology. Moreover, a dynamic updating mechanism by niching PSO is proposed to guarantee the parallel searching capability during the whole evolution process. Thus, this proposed algorithm might be problem-independent and search-stage-independent, yielding more satisfying solutions on various optimization problems. A comprehensive experimental study is conducted on 28 benchmark functions of CEC 2013 special session on real-parameter optimization, including shifted, rotated, multi-modal, high conditioned, expanded and composition problems, compared with several state-of-the-art variants of PSO and differential evolution (DE) algorithms. Comparison results show that SaHSPS obtains outstanding performances on the majority of the test problems. Moreover, a practical engineering problem, real power loss minimization of IEEE 30-bus power system, is used to further evaluate SaHSPS. The numerical results, compared with other stochastic search algorithms, show that SaHSPS could find high-quality solutions with higher probability.