Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River

The Hulu Langat basin, a strategic watershed in Malaysia, has in recent decades been exposed to extensive changes in land-use and consequently hydrological conditions. In this work, the impact of Land Use and Cover Change (LUCC) on hydrological conditions (water discharge and sediment load) of the basin were investigated using the Soil and Water Assessment Tool (SWAT). Four land-use scenarios were defined for land-use change impact analysis, i.e. past, present (baseline), future and water conservation planning. The land-use maps, dated 1984, 1990, 1997 and 2002, were defined as the past scenarios for LUCC impact analysis. The present scenario was defined based on the 2006 land-use map. The 2020 land-use map was simulated using a cellular automata-Markov model and defined as the future scenario. Water conservation scenarios were produced based on guidelines published by Malaysia’s Department of Town and Country Planning and Department of Environment. Model calibration and uncertainty analysis was performed using the Sequential Uncertainty Fitting (SUFI-2) algorithm. The model robustness for water discharge simulation for the period 1997–2008 was good. However, due to uncertainties, mainly resulting from intense urban development in the basin, its robustness for sediment load simulation was only acceptable for the calibration period 1997–2004. The optimized model was run using different land-use maps over the periods 1997–2008 and 1997–2004 for water discharge and sediment load estimation, respectively. In comparison to the baseline scenario, SWAT simulation using the past and conservative scenarios showed significant reduction in monthly direct runoff and monthly sediment load, while SWAT simulation based on the future scenario showed significant increase in monthly direct runoff, monthly sediment load and groundwater recharge.Editor D. Koutsoyiannis; Associate editor C. Perrin

Welded structures are widely used in many engineering load carrying structures such as columns, towers for wind turbine or water tanks, offshore and submarine structures, girders, stiffened doors, etc. welding is a costly fabrication process and proper sequence and welding process effect the cost of a huge structure. Keeping this in view two different problems from the literature for the cost optimization of welded structures are considered in this paper. The optimization procedure is carried out using artificial bee colony (ABC) optimization technique. Classical ABC is modified to increase the convergence rate of the original algorithm. Comparison of both the variants is experimented on many bench mark examples from the literature and also on two cost optimization problems of welded structures. The results of the considered techniques are compared with the previously published results. The considered techniques have given much better results in comparison to the previously tried approaches and also modified ABC has shown superiority over classical ABC.

The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. The comparison of different input vectors showed improvements in the prediction of sediments by using the snow cover area in addition to flows, effective rainfall, temperature, and evapotranspiration. Overall, the ANN model performed better than all other models. However, as regards sediment load peak time series, the sediment loads predicted using the ANN, ANFIS-FCM, and MARS models were found to be closer to the measured sediment loads. The ANFIS-FCM performed better in the estimation of peak sediment yields with a relative accuracy of 81.31% in comparison to the ANN and MARS models with 80.17% and 80.16% of relative accuracies, respectively. The developed multiple linear regression equation of all models show an R2 value of 0.85 and 0.74 during the training and testing period, respectively.

Harmonic distortion presents a significant challenge in renewable energy integration, particularly in conventional 2-level inverters. Multilevel inverters, such as the cascaded H-bridge topology, offer an effective solution by generating multiple voltage levels, thereby reducing distortion and eliminating the need for bulky filters. This study investigates harmonic reduction in a single-phase 21-level asymmetric cascaded H-bridge multilevel inverter using a novel hybrid optimization algorithm combining Artificial Bee Colony (ABC) and Firefly Algorithm (FA). The hybrid ABC-FA algorithm is designed to determine optimal switching angles for minimizing Total Harmonic Distortion (THD) while addressing the limitations of conventional heuristic methods. Modeled and simulated in MATLAB/SIMULINK, the proposed algorithm demonstrates enhanced performance in both harmonic reduction and convergence speed. Simulation results show that the hybrid ABC-FA algorithm achieves THD levels below 5%, representing a 10–20% improvement over standalone ABC or FA implementations. Additionally, the algorithm exhibits faster convergence, highlighting its effectiveness and reliability for improving power quality and facilitating efficient integration of renewable energy sources into the electrical grid.

Volterra kernel based face recognition using artificial bee colonyoptimization

Swarm intelligence is briefly defined as the collective behavior of decentralized and self-organized swarms. Self-organization and labor division are the two key components of swarm intelligence. Artificial Bee Colony (ABC) algorithm is one of the most recent swarm intelligence-based algorithms. The behavior of bees in ABC algorithm satisfies the self-organization features, but there is no specific labor division mechanism in ABC algorithm. In this work, we propose an improved ABC algorithm called labor division artificial bee colony (LDABC) algorithm by incorporating the labor division mechanism into ABC algorithm, which is achieved by individual specialization and role plasticity. We specify three different search methods for employed bees, onlooker bees and scout bees to realize individual specialization, these search methods are related to food source quality, enable bees to maximize exploitation of food source. Role plasticity is achieved by combining with cellular automata, where the roles of bees are not static but vary with their surrounding environment, enable bees not to limit to one search method. The different search modes and the flexibility of the search behaviors make our algorithm achieve a better balance between exploration and exploitation. The experimental results tested on 13 benchmark functions and CEC-2013 test functions demonstrate a competitive performance.

The artificial bee colony (ABC) algorithm is an important branch of evolutionary algorithms and has been shown to be competitive with other algorithms for solving optimisation problems. However, there are always some insufficiencies in ABC algorithm such as lower convergence speed and easily get trapped in the local optima. To increase depth search capabilities of onlooker bees and ensure scout bees do not discard the current optimal solution, we proposed a modified ABC algorithm (denoted as ORABC) based on the optimisation strategy and retained strategy of the best individual in this paper. Compared with ABC algorithm, ABC* (added retained strategy to ABC) and ORABC algorithm, experiments are conducted on a set of numerical benchmark functions. The numerical simulation results demonstrate that ORABC algorithm improves the convergence characteristics of ABC algorithm and provides very remarkable performance in solving complex numerical optimisation problems compared to original algorithm.

The Software Cost Estimation (SCE) is one of most important issues in the cycle of development, management decision, and in the quality of software project. In the case of the lack of certainty of the exact cost for software projects' development, companies encounter with numerous challenges. Also, due to inaccurate cost estimates, making wrong decisions by project managers make irreparable damage. To reach this propos, the effective factors for developing software projects should be evaluated to ensure of the projects success. COCOMO model is the main model for SCE which acts based on criteria and quantities such as number of Line of Code (LOC) or the Function Point Analysis (FPA). Research in recent years has shown that COCOMO model has not a good performance in the SCE. In this paper, we studied SCE by using a hybrid of Genetic Algorithm (GA) and Artificial Bee Colony (ABC) which are Meta-Heuristic Algorithms. Test results show that proposed model, GA and ABC algorithms have less MRE errors values than the COCOMO model. Also, the hybrid model has better convergence comparing with the GA and ABC algorithms.

Optimizing the location of distributed generation units in electrical distribution networks is a well-established approach to improving grid performance, particularly for enhancing reliability and reducing operational losses. Recent large-scale blackouts in Spain and Portugal have underscored the vulnerability of power systems and the urgent need for more resilient, cost-effective, and efficient grid configurations. However, integrating distributed generation involves significant investment, operational, and maintenance costs, making optimal placement a critical challenge. Integrating optimally located distributed generation units into distribution networks using a modified artificial bee colony algorithm will substantially reduce power losses, voltage deviations, and total electricity costs compared to the conventional artificial bee colony and other state-of-the-art optimization methods. The main contributions of this work are: (1) the development of an enhanced artificial bee colony algorithm incorporating inverse population initialization and a bounded-round search strategy to improve diversity and avoid premature convergence; (2) the introduction of a chaotic cosine-based neighborhood search around the global best to accelerate convergence and enhance solution quality; and (3) the application and validation of the proposed method on IEEE 33-bus and 69-bus distribution systems, demonstrating its effectiveness in reducing power losses and improving voltage profiles. The proposed algorithm achieved reductions in total power loss of up to 82.33% for the IEEE 33-bus system and 86.6% for the IEEE 69-bus system, outperforming the basic artificial bee colony and other recent optimization techniques. The findings confirm that the modified artificial bee colony algorithm is a robust and efficient tool for distributed generation allocation, providing significant improvements in grid resilience, reliability, and operational efficiency in the face of increasing energy demands and instability risks.

INTRODUCTION: The artificial bee colony (ABC) algorithm is a nature-inspired technique used for solving different optimization problems. This paper presents a multi-hive ABC algorithm for solving constrained benchmark functions of CEC2006. The CEC2006 data set contains the global benchmark functions with different design variables, number and type of constraints. OBJECTIVES: The objective of the proposed work is to design and apply the GPGPU based multi-hive ABC algorithm to solve constrained optimization problems. METHODS: The proposed approach is a multi-population coarse-grained system implemented using General Purpose Graphics Processing Unit (GPGPU). The performance of the proposed approach is compared with the serial ABC algorithm for eleven benchmark functions and results in the literature. The multi-hive ABC algorithm has multiple hives, each running separate ABC algorithm on different cores of GPGPU. RESULTS: The proposed approach provides global best solutions in significantly reduced time for all benchmark functions. The speed-up obtained is approximately 7X to 9X. The GPGPU device utilization is approximately 57% to 91%. CONCLUSION: The GPGPU based multi-hive ABC algorithm is a found good with respect to best results, speed up factor and GPU utilization to solve constrained optimization problems.

Swarm intelligence approaches to estimate electricity energy demand in Turkey

The electrical power is transmitted through a high-voltage transmission system to low-voltage consumers on the distribution side. Because of the high R/X ratio, high current, and low voltage in distribution systems, active power loss (TPL) is much higher as compared to transmission systems. The economic incentive for distribution power utilities is to reduce losses in their networks. This inducement is generally the cost differential between actual and standard losses. As a result, when actual losses exceed standard losses, the utilities are economically penalized, while when the reverse happens, they are getting benefitted. As a result, the loss minimization problem in distribution systems is an excellent research subject for academics. All the methods for the loss minimization are different from each other by the means of formulation of the problem, tools used to minimize the losses, constraints taken, solution methods, and results are obtained. In most of the cases, the distribution system is interconnected and due to increment in load demand and limitations of loading capacity of distribution lines, the idea of distribution side generation came to maintain the system parameters like voltage profile, power loss, cost of operation, etc. To minimize losses, several methods are available like distributed generation (DG) using renewable energy sources and combined heat and power systems, capacitor allocation, reconfiguration of the network, and D-FACTS devices like dynamic voltage restorer (DVR), distribution static compensator (D-STATCOM), unified power quality conditioner (UPQC) allocation, etc. In this chapter, 33 and 69 bus mesh distribution system analysis is proposed with implementation of multiple wind turbines (WT) and multiple D-STATCOM. Advantages of WT are cost-effective generation, clean fuel source, sustainability, and less land requirement. Here, the Artificial Bee Colony (ABC) algorithm is used for finding optimal size and location of WG and D-STATCOM. ABC is an optimization technique that offers population-based search procedure in which artificial bees change individual food locations over time, with the aim of the bees being to locate the places of food supply with the most nectar and recognize it as the one with the greatest nectar. Here direct approach-based load flow method is used to find load flow parameters like bus voltage, branch currents, and power loss. The outcome verifies effective improvement in the system voltage profile, reduction in distribution feeder line loss, and increment in cost savings.

Optimal Design of Fuzzy Fractional Order PIλDµ Controller for Redundant Robot

Consensus Algorithm-based Coalition Game Theory for Demand Management Scheme in Smart Microgrid

Wireless sensor networks (WSNs) currently play an important role due to their variety of applications in several fields. Improving the overall WSN performance and avoiding its limitations and challenges have become the subject of many researchers. One of the main critical issues is transmitted data security. A sinkhole attack is one of the most dangerous attacks that can be used as a platform for starting several attacks. It has a direct influence on network performance in terms of information confidentiality, integrity, and even availability. An effective method to detect such an attack leads to improving the overall WSN performance as well as enhancing the sent data secrecy. This article introduces an enhanced intrusion detection system (IDS) to protect WSNs from sinkhole attacks. The IDS is modified with a reputation-based mechanism to make it compatible with WSN requirements. An artificial bee colony (ABC) optimization technique is implemented to improve the IDS performance. Noisy channels are added to take into consideration the nature of the WSN. The proposed system achieves more than 97% overall accuracy and a detection rate of 98% with a false positive rate of less than 1.7%, which seems superior to the results of previous works.