A Comprehensive Improved Salp Swarm Algorithm on Redundant Container Deployment Problem

Improved salp swarm algorithm combined with chaos

Reasonable optimal operation policy for complex multiple reservoir systems is very important for the safe and efficient utilization of water resources. The operation policy of multiple hydropower reservoirs should be optimized to maximize total hydropower generation, while ensuring flood control safety by effective and efficient storage and release policy of multiple reservoirs. To achieve this goal, a new meta-heuristic algorithm, salp swarm algorithm (SSA), is used to optimize the joint operation of multiple hydropower reservoirs for the first time. SSA is a competitive bio-inspired optimizer, which has received substantial attention from researchers in a wide variety of applications in finance, engineering, and science because of its little controlling parameters and adaptive exploratory behavior. However, it still faces few drawbacks such as lack of exploitation and local optima stagnation, leading to a slow convergence rate. In order to tackle these problems, multiple strategies combining sine cosine operator, opposition-based learning mechanism, and elitism strategy are applied to the original SSA. The sine cosine operator is applied to balance the exploration and exploitation over the course of iteration; the opposition-based learning mechanism is used to enhance the diversity of the swarm; and the elitism strategy is adopted to find global optima. Then, the improved SSA (ISSA) is compared with six well-known meta-heuristic algorithms on 23 classical benchmark functions. The results obtained demonstrate that ISSA outperforms most of the well-known algorithms. Then, ISSA is applied to optimal operation of multiple hydropower reservoirs in the real world. A multiple reservoir system, namely Xiluodu Reservoir and Xiangjiaba Rservoir, in the upper Yangtze River of China are selected as a case study. The results obtained show that the ISSA is able to solve a real-world optimization problem with complex constraints. In addition, for the typical flood with a 100 return period in 1954, the maximum hydropower generation of multiple hydropower reservoirs is about 6671 GWh in the case of completing the flood control task, increasing by 1.18% and 1.77% than SSA and Particle Swarm Optimization (PSO), respectively. Thus, ISSA can be used as an alternative effective and efficient tool for the complex optimization of multiple hydropower reservoirs. The water resources in the river basin can be further utilized by the proposed method to cope with the increasingly serious climate change.

Metaheuristic algorithms are one of the methods used to solve optimization problems and find global or close to optimal solutions at a reasonable computational cost. As with other types of algorithms, in metaheuristic algorithms, one of the methods used to improve performance and achieve results closer to the target result is the hybridization of algorithms. In this study, a hybrid algorithm (HSSJAYA) consisting of salp swarm algorithm (SSA) and jaya algorithm (JAYA) is designed. The speed of achieving the global optimum of SSA, its simplicity, easy hybridization and JAYA's success in achieving the best solution have given us the idea of creating a powerful hybrid algorithm from these two algorithms. The hybrid algorithm is based on SSA's leader and follower salp system and JAYA's best and worst solution part. HSSJAYA works according to the best and worst food source positions. In this way, it is thought that the leader-follower salps will find the best solution to reach the food source. The hybrid algorithm has been tested in 14 unimodal and 21 multimodal benchmark functions. The results were compared with SSA, JAYA, cuckoo search algorithm (CS), firefly algorithm (FFA) and genetic algorithm (GA). As a result, a hybrid algorithm that provided results closer to the desired fitness value in benchmark functions was obtained. In addition, these results were statistically compared using wilcoxon rank sum test with other algorithms. According to the statistical results obtained from the results of the benchmark functions, it was determined that HSSJAYA creates a statistically significant difference in most of the problems compared to other algorithms.

Metaheuristic algorithms are often applied to global function optimization problems. To overcome the poor real-time performance and low precision of the basic salp swarm algorithm, this paper introduces a novel hybrid algorithm inspired by the perturbation weight mechanism. The proposed perturbation weight salp swarm algorithm has the advantages of a broad search scope and a strong balance between exploration and exploitation and retains a relatively low computational complexity when dealing with numerous large-scale problems. A new coefficient factor is introduced to the basic salp swarm algorithm, and new update strategies for the leader position and the followers are introduced in the search phase. The new leader position updating strategy has a specific bounded scope and strong search performance, thus accelerating the iteration process. The new follower updating strategy maintains the diversity of feasible solutions while reducing the computational load. This paper describes the application of the proposed algorithm to low-dimension and variable-dimension functions. This paper also presents iteration curves, box-plot charts, and search-path graphics to verify the accuracy of the proposed algorithm. The experimental results demonstrate that the perturbation weight salp swarm algorithm offers a better search speed and search balance than the basic salp swarm algorithm in different environments.

With the increasing demand for hospital services amidst the COVID-19 pandemic, allocation of limited public resources and management of healthcare services are of paramount importance. In the field of patient flow scheduling, previous research primarily focused on classical-based objective functions, while ignoring environmental-based objective functions. This study presents a flexible job shop scheduling problem to optimize patient flow and, thereby, minimize the total carbon footprint, as the sustainability-based objective function. Since flexible job shop scheduling is an NP-hard problem, a metaheuristic optimization algorithm, called Chaotic Salp Swarm Algorithm Enhanced with Opposition-Based Learning and Sine Cosine (CSSAOS), was developed. The proposed algorithm integrates the Salp Swarm Algorithm (SSA) with chaotic maps to update the position of followers, the sine cosine algorithm to update the leader position, and opposition-based learning for a better exploration of the search space. generating more accurate solutions. The proposed method was successfully applied in a real-world case study and demonstrated better performance than other well-known metaheuristic algorithms, including differential evolution, genetic algorithm, grasshopper optimization algorithm, SSA based on opposition-based learning, quantum evolutionary SSA, and whale optimization algorithm. In addition, it was found that the proposed method is scalable to different sizes and complexities.

The fast development of electric vehicles (EVs) has resulted in several topics of research in this area, such as the development of a charging pricing strategy, charging control, location of the charging station, and the structure within the charging station. This paper proposes the optimal design of the structure of an EV fast-charging station (EVFCS) connected with a renewable energy source and battery energy storage systems (BESS) by using metaheuristic algorithms. The optimal design of this structure aims to find the number and power of chargers. Moreover, the renewable energy source and BESS can reduce the impact on the grid, so these energy sources are considered as ones of the optimally-designed structure of EVFCS in this work. Thus, it is necessary to determine the optimal sizing of the renewable energy source, BESS, and the grid power connected to EVFCS. This optimal structure can improve the profitability of the station. To solve the optimization problem, three metaheuristic algorithms, including particle swarm optimization (PSO), Salp swarm algorithm (SSA), and arithmetic optimization algorithm (AOA), are adopted. These algorithms aim to find the optimal structure which maximizes the profit of the EVFCS determined by its net present value (NPV), and the results obtained from these algorithms were compared. The results demonstrate that all considered algorithms could find the feasible solutions of the optimal design of the EVFCS structure where PSO provided the best NPV, followed by AOA and SSA.

In order to solve the problem that the conventional tracker is not adapted to the abrupt motion, a tracking algorithm based on the improved salp swarm algorithm (ISSA) was proposed. Visual tracking is considered to be a process of locating the optimal position through the interaction between leaders and followers in successive images. Firstly, the adaptive mechanism of leader and follower is introduced into the original salp swarm algorithm (SSA) to balance the exploitation and exploration of the algorithm. This method can improve the accuracy and effect of tracking. Secondly, the golden-sine algorithm was used to update the position of followers, considering that the SSA had a single spatial search mode for followers and was easy to fall into the local optimum. By comparing with 19 classical tracking algorithms, qualitative and quantitative analysis is carried out to verify the tracking effect of the proposed method. A large number of experimental results show that the algorithm proposed here has good performance in visual tracking, especially for mutation motion tracking.

Adaptive levy-assisted salp swarm algorithm: Analysis and optimization case studies

Swarm-based algorithm is best suitable when it can perform smooth balance between the exploration and exploitation as well as faster convergence by successfully avoiding local optima entrapment. At recent time, salp swarm algorithm (SSA) is developed as a nature-inspired swarm-based algorithm. It can solve continuous, nonlinear and complex in nature day-to-day life optimization problems. Like many other optimization algorithms, SSA suffers with the problem of local stagnation. This paper introduces an improved version of the SSA, which improves the performance of the existing SSA by using space transformation search (STS). The proposed algorithm is termed as STS-SSA. The STS-SSA enhances the exploration and exploitation capability in the search space and successfully avoids local optima entrapment. The STS-SSA is evaluated by considering the IEEE CEC 2017 standard benchmark function set. The efficiency and robustness of the proposed STS-SSA are measured using performance metrics, convergence analysis and statistical significance. A demonstration is given as an application of the proposed algorithm for solving a real-life problem. For this purpose, the multi-layer feed-forward network is trained using the proposed STS-SSA. The experimental results demonstrate that the developed STS-SSA can be used for solving optimization problems effectively.

Enhanced salp swarm algorithm: Application to variable speed wind generators

A prediction method based on an improved salp swarm algorithm (ISSA) and extreme learning machine (ELM) was proposed to improve line heating and forming. First, a three-dimensional transient numerical simulation of line heating and forming was carried out by applying a finite element simulation, and the influence of machining parameters on deformation was studied. Second, a prediction model for the ELM network was established based on simulation data, and the deformation of hull plate was predicted by the training network. Additionally, swarm intelligence optimization, particle swarm optimization (PSO), the seagull optimization algorithm (SOA), and the salp swarm algorithm (SSA) were studied while considering the shortcomings of the ELM, and the ISSA was proposed. Input weights and hidden layer biases of the ELM model were optimized to increase the stability of prediction results from the PSO, SOA, SSA and ISSA approaches. Finally, it was shown that the prediction effect of the ISSA–ELM model was superior by comparing and analyzing the prediction effect of each prediction model for line heating and forming.

Grey-Wolf Optimizer (GWO) and Salp-Swarm Algorithm (SSA) are among the recent metaheuristic algorithms that have shown considerable potential in solving practical problems. Nevertheless, these optimisation methods usually suffer from a poor performance, especially when dealing with multimodal and high-dimensional problems. To overcome drawbacks of these algorithms, a new population-based global optimisation algorithm inspired by GWO and SSA is proposed in this paper. The basic idea behind the proposed approach is to divide the population into two groups that adopt new position updating strategies. The movement method of the first group is inspired by the hunting mechanism used in GWO algorithm. On the other hand, the population of the second group moves into the search space using a new strategy based on the position updating method employed by the follower salps in SSA. The performances of the proposed algorithm, named GW-SSA, have been studied through 23 benchmark functions of different types and dimensions. The obtained results were statistically analysed using Wilcoxon signed-rank. Furthermore, GW-SSA algorithm has been applied to solve four real engineering design problems. The experimental results show the superior performance of GW-SSA compared to GWO, SSA and other recently proposed optimisation algorithms.

This paper addresses the economic dispatch problem in thermal power systems using four metaheuristic optimization algorithms: Particle Swarm Optimization (PSO), Crow Search Algorithm (CSA), Salp Swarm Algorithm (SSA), and JAYA algorithm. A deterministic formulation is adopted to minimize the total generation cost over a 24-hour horizon while meeting generator operating constraints and ensuring load balance. A randomly generated dispatch strategy is also included as a baseline. Each algorithm is independently executed 100 times to evaluate robustness, repeatability, and associated CO2 emissions. Among all methods, PSO achieves the best performance, yielding the lowest total dispatch cost of $82,412.78 and the smallest relative standard deviation (0.12%), along with total CO2 emissions of 1901.65 kg. Compared to other techniques, PSO provides cost improvements of 0.20% over CSA, 0.28% over SSA, 0.94% over JAYA, and a substantial 29.23% reduction with respect to the random baseline. Moreover, all metaheuristic strategies significantly outperform the random dispatch, demonstrating their ability to generate high-quality and feasible solutions. The PSO-based dispatch strategy efficiently allocates hourly power outputs within technical constraints, introducing a controlled overgeneration margin to compensate for system losses. These results confirm the effectiveness of metaheuristic approaches in complex power system optimization tasks and establish a foundation for future work involving renewable integration, emission constraints, and uncertainty modeling.

In this paper, an accurate model of proton exchange membrane fuel cell (PEMFC) for optimal identification of PEMFC parameters has been developed. The optimization methodology is based on the modified version of Manta Ray Foraging Optimization (MMRFO) technique for minimizing the sum of squared errors (SSE) between the Experimentally measured stack voltage and the estimated voltage produced by the optimized model. In the modified methodology, the sine-cosine method has been utilized to enhance the global searching capability in the exploration phase and the local searching capability in the exploitation phase of the MRFO algorithm. In order to validate the effectiveness of the suggested methodology, four different case studies comprising standard benchmark 250 W PEMFC, BCS-500 W PEMFC, SR-12 500 W FC, and 1 kW Temasek stacks were utilized, and the attainments have been compared with the measured polarization characteristics. The attainments have been intensively compared with several metaheuristic algorithms (MA) including Tree growth Algorithm (TGA), Grey wolf optimizer (GWO), Whale optimization algorithm (WOA), Salp swarm algorithm (SSA), and original Manta Ray Foraging Optimization (MRFO), to confirm the superiority of the MMRFO against the compared techniques. The obtained results give a satisfactory agreement between the MMRFO-based model and the experimentally measured data. Finally, the achievements confirmed the effectiveness of the MMRFO over the basic MRFO algorithm and other novel metaheuristic algorithms in identifying PEMFC parameters.

Global solar radiation prediction over North Dakota using air temperature: Development of novel hybrid intelligence model