Spillway design poses a significant challenge in effectively managing the energy within water flow to prevent erosion and destabilization of dam structures. Traditional approaches typically advocate for standard hydraulic jump stilling basins or other energy dissipators at spillway bases yet constructing such basins can be prohibitively large and costly, particularly when extensive excavation is necessary. Consequently, growing interest in cascade hydraulic structures has emerged over recent decades as an alternative for energy dissipation. These structures utilize a series of arranged steps to facilitate water flow, effectively dissipating energy as it traverses the cascade. Commonly deployed in scenarios involving high dams or steep gradients, the stepped configuration ensures efficient aeration and substantial energy dissipation along the structure, thereby reducing the size and cost of required stilling basins. Despite extensive research on hydraulic characteristics using physical and numerical models and established design procedures, construction cost optimization of step cascades remains limited but promising. This paper aims to address this gap by employing two novel gradient-based meta-heuristic optimization techniques to enhance the efficiency and cost-effectiveness of cascade stilling basin designs. Through comparative analyses and evaluations, this study demonstrates the efficacy of these techniques and offers insights for future research and applications in hydraulic structures design optimization.

In the present paper, seismic performance-based optimization of steel frames is implemented using a number of advanced meta-heuristic algorithms. The optimization process is implemented by two newly developed meta-heuristics, dolphin echolocation optimization (DEO) and enhanced colliding bodies optimization (ECBO). In the present study, a slight modification is achieved on the ECBO to improve its convergence rate and therefore it is named here as ECBO-II. Furthermore, the results of DEO, ECBO and ECBO-II are compared with those of particle swarm optimization (PSO). Evaluating the structural responses by nonlinear time-history analysis during the optimization process is very time consuming. In order to reduce the computational rigor, radial basis function (RBF) neural network is utilized to predict the necessary structural time-history responses during the optimization process. Two numerical examples are presented to illustrate the efficiency of the meta-heuristics for tackling the seismic performance-based optimization problems.

Cloud Manufacturing (CMfg) enables flexible and customized manufacturing services through dynamic service composition. However, achieving optimal service composition remains challenging due to the need to meet complex Quality of Service (QoS) requirements, including cost, time, quality, and resource workload balance. Notably, previous studies on service composition models have rarely considered workload balancing as part of their QoS criteria, which is critical for maintaining efficient and sustainable resource use. This study addresses this gap by presenting an advanced service composition model that integrates workload balance as an essential QoS metric alongside traditional factors like composite service quality, time, and cost. To further support optimization, the Simulated Annealing (SA) and Tabu Search (TS) algorithms are enhanced with a novel shaking mechanism designed to expand the search space and mitigate premature convergence risks common in metaheuristics. Experimental evaluations conducted on an OR-Library dataset confirm that the enhanced SA algorithm achieves up to a 25% improvement in the fitness function and a 7% reduction in computational time, while the improved TS algorithm achieves a 2% reduction in the fitness function and a 21% decrease in computational time. These findings highlight the model's potential to enhance CMfg service composition efficiency, offering substantial performance benefits over traditional methods. The core contributions of this study include the development of a workload-integrated service composition model and enhancements to SA and TS algorithms for effective problem-solving within this framework.

In the present study, the computational performance of the particle swarm optimization (PSO) harmony search (HS) and firefly algorithm (FA), as popular metaheuristics, is investigated for size and shape optimization of truss structures. The PSO was inspired by the social behavior of organisms such as bird flocking. The HS imitates the musical performance process which takes place when a musician searches for a better state of harmony, while the FA was based on the idealized behavior of the flashing characteristics of natural fireflies. These algorithms were inspired from different natural sources and their convergence behavior is focused in this paper. Several benchmark size and shape optimization problems of truss structures are solved using PSO, HS and FA and the results are compared. The numerical results demonstrate the superiority of FA to HS and PSO.

The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem that allows to process operations on one machine out of a set of alternative machines. The FJSP is an NPhard problem consisting of two sub-problems, which are the assignment and the scheduling problems. In this paper, we propose how to solve the FJSP by hybrid metaheuristics-based clustered holonic multiagent model. First, a neighborhood-based genetic algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Second, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. The efficiency of our approach is explained by the flexible selection of the promising parts of the search space by the clustering operator after the genetic algorithm process, and by applying the intensification technique of the tabu search allowing to restart the search from a set of elite solutions to attain new dominant scheduling solutions. Computational results are presented using four sets of well-known benchmark literatureinstances. New upper bounds are found, showing the effectiveness of the presented approach.

Sudden and severe stock price crashes pose a significant challenge to capital markets. The substantial losses incurred from such events underscore the need for more effective risk forecasting tools. This study aims to enhance the predictive power of risk models for stock price declines in the Tehran Stock Exchange and commenced with a comprehensive literature review to identify key financial factors influencing stock price volatility. Given the high dimensionality of the dataset and the extended time period, metaheuristic algorithms were employed for feature selection. 10 algorithms, namely Ant Colony Optimization, Hill Climbing, Las Vegas, Whale Optimization, Simulated Annealing, Genetic Algorithm, Tabu Search, Particle Swarm Optimization (PSO), Honey Bee (HBA) and Firefly were utilized to reduce dimensionality and enhance model performance. Five variables, namely "Return on Equity," "Debt Ratio," "Cash Flow to Income Ratio," "Negative Skewness of Stock Returns," and "Logarithm of Sales," were selected based on the outcomes of metaheuristic algorithms. Attention to these five variables is of great importance for economic actors and investors; these variables serve as key indicators in analyzing the financial status and performance of companies and can assist in identifying potential risks. Subsequently, ANNs were implemented to develop predictive models. The models were trained and evaluated using historical data from the Tehran Stock Exchange spanning from 2001 to 2020. The findings of this research demonstrate that combining metaheuristic algorithms for model reduction and optimization, along with advanced machine learning techniques, yields results that can significantly improve risk management and investment decision-making.

Using linear, nonlinear, and dynamic planning methods for water resources management has been common since a long time ago, but owing to some deficiencies, today much attention is paid to heuristics methods. Among the optimization algorithms, the mothfire algorithm can be considered. In this paper, the optimization of the flood management plan was carried out using the moth-fire algorithm. In order to consider the flood damage in each month, the estimated damage values are determined according to the floods routing with different return periods in the downstream of the dam using MATLAB software. The sum of the expected damage of flood and lack of need supply in the objective function will be minimized using the moth-fire algorithm. The results of a case study carried out on the Aras dam indicate the efficiency of the proposed optimization model in supplying the needs and reducing the flood damage in the downstream.