Multi-objective Salp Swarm Algorithm Research Articles

Multi-Verse Algorithm based Approach for Multi-criteria Path Planning of Unmanned Aerial Vehicles

In this paper, a method based on a Multiobjective Multi-Verse Optimizer (MOMVO) is proposed and successfully implemented to solve the unmanned aerial vehicles’ path planning problem. The generation of each coordinate of the aircraft is reformulated as a multiobjective optimization problem under operational constraints. The shortest and smoothest path by avoiding all obstacles and threats is the solution of such a hard optimization problem. A set of competitive metaheuristics such as Multiobjective Salp Swarm Algorithm (MSSA), Grey Wolf Optimizer (MOGWO), Particle Swarm Optimization (MOPSO) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) are retained as comparison tools for the problem’s resolution. To assess the performance of the reported algorithms and conclude about their effectiveness, an empirical study is firstly performed for solving different multiobjective test functions from the literature. These algorithms are then used to obtain a set of optimal Pareto solutions for the multi-criteria path planning problem. An efficient Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) of Multi Criteria Decision-Making (MCDM) model is investigated to find the optimal solution from the non-dominant ones. Demonstrative results and statistical analysis are presented and compared in order to show the effectiveness of the proposed MOMVO-based path planning technique.

Multiobjective big data optimization based on a hybrid salp swarm algorithm and differential evolution

This paper developed a multiobjective Big Data optimization approach based on a hybrid salp swarm algorithm and the differential evolution algorithm. The role of the differential evolution algorithm is to enhance the capability of the feature exploitation of the salp swarm algorithm because the operators of the differential evolution algorithm are used as local search operators. In general, the proposed method contains three stages. In the first stage, the population is generated, and the archive is initialized. The second stage updates the solutions using the hybrid salp swarm algorithm and the differential evolution algorithm, and the final stage determines the nondominated solutions and updates the archive. To assess the performance of the proposed approach, a series of experiments were performed. A set of single-objective and multiobjective problems from the 2015 Big Data optimization competition were tested; the dataset contained data with and without noise. The results of our experiments illustrated that the proposed approach outperformed other approaches, including the baseline nondominated sorting genetic algorithm, on all test problems. Moreover, for single-objective problems, the score value of the proposed method was better than that of the traditional multiobjective salp swarm algorithm. When compared with both algorithms, that is, the adaptive DE algorithm with external archive and the hybrid multiobjective firefly algorithm, its score was the largest. In contrast, for the multiobjective functions, the scores of the proposed algorithm were higher than that of the fireworks algorithm framework.

Prediction of processing time and energy consumption and optimization of machining parameters in gear hobbing

This paper studied the characteristics of time and energy consumption of gear hobbing in batch production of small modulus gear. A method of integrating design of experiment, response surface method and multi-objective salp swarm algorithm (DOE/RSM/MMSA) is proposed for the complex optimization of machining parameters (namely hob rotation speed, cutting feed and depth of cut) in gear hobbing process. This paper uses three-level factorial design method to design gear hobbing experiments, and carries out mathematical and statistical analysis on the experimental results. It is found that cutting feed is the most significant machining parameter followed by hob rotation speed and depth of cut to reduce processing time and energy consumption. The prediction model of processing time and energy consumption in gear hobbing process is obtained through response surface methodology. The relative error rates of the predicted value and the actual value of the processing time and energy consumption are 0.11% and 0.09% respectively, indicating the validity of the model. Multi-objective salp swarm algorithm is used to optimize the machining parameters to minimizing processing time and energy consumption. Finally, through the comparison with the existing research results, it is concluded that the optimized machining parameters have better processing effect to achieve minimized processing time and energy consumption, which shows the effectiveness and rationality of the method proposed, and provides references for the decision-making of machining parameters in workshop.

Optimal Design of Squirrel Cage Induction Motor Using Multi-Objective Salp Swarm Algorithm

Optimal Design of Squirrel Cage Induction Motor Using Multi-Objective Salp Swarm Algorithm

Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm

The sink nodes in large-scale wireless sensor networks (LSWSNs) are responsible for receiving and processing the collected data from sensor nodes. Identifying the locations of sink nodes in LSWSNs play a vital role in term of saving energy. Furthermore, sink nodes have extremely extra resources such as large memory, powerful batteries, long-range antenna, etc. This paper proposes a multi-objective whale optimization algorithm (MOWOA) to determine the lowest number of sink nodes that cover the whole network. The major aim of MOWOA is to reduce the energy consumption and prolongs the lifetime of LSWSNs. To achieve these objectives, a fitness function has been formulated to decrease energy consumption and maximize the network’s lifetime. The experimental results revealed that the proposed MOWOA achieved a better efficiency in reducing the total power consumption by 26% compared with four well-known optimization algorithms: multi-objective grasshopper optimization algorithm, multi-objective salp swarm algorithm, multi-objective gray wolf optimization, multi-objective particle swarm optimization over all networks sizes.

Multi-objective algorithm for the design of prediction intervals for wind power forecasting model

• A new algorithm based on multi-objective formulation is applied to design the prediction intervals for wind power. • Data pre-process strategy based on feature extraction is built to reduce the complexity and determine the input forms. • The wind speed prediction intervals are estimated through machine learning method . • Fuzzy set theory selection method is applied to extract the best compromise solution. A composite forecasting framework is designed and implemented successfully to estimate the prediction intervals of wind speed time series simultaneously through machine learning method embedding a newly proposed optimization method (multi-objective salp swarm algorithm). In this study, data pre-process strategy based on feature extraction is served for reducing the fluctuations of wind power generation and select appropriate input forms of wind speed datasets for the sake of improving the overall performance. Besides, fuzzy set theory selection technique is used to determine the best compromise solutions from Pareto front set deriving from the optimization phase. To test the effectiveness of the proposed composite forecasting framework, several case studies based on different time-scale wind speed datasets are conducted. The corresponding results present that the proposed framework significantly outperforms other benchmark methods, and it can provide very satisfactory results in both goals between high coverage and small width.

The study and application of a novel hybrid system for air quality early-warning

Air quality early-warning plays a vital role in improving air quality and human health, especially multi-step ahead air quality early-warning, which is significant for both citizens and environmental protection departments. However, most previous studies have only employed simple data decomposition to perform one-step forecasting and were aimed at enhancing forecasting accuracy or stability. Little research has improved these two standards simultaneously, leading to poor forecasting performance. Because of its significance, relevant research focused on multi-step ahead air quality early-warning is especially needed. Therefore, in this paper, a novel hybrid air quality early-warning system, which consists of four modules: data preprocessing module, optimization module, forecasting module and evaluation module, is proposed to perform multi-step ahead air quality early-warning. In this system, an effective data decomposition method called the modified complete ensemble empirical mode decomposition with adaptive noise is developed to effectively extract the characteristics of air quality data and to further improve the forecasting performance. Moreover, the hybrid Elman neural network model, optimized by the multi-objective salp swarm algorithm, is successfully developed in the forecasting module and simultaneously achieves high forecasting accuracy and stability. In addition, the evaluation module is designed to conduct a reasonable and scientific evaluation for this system. Three cities in China are employed to test the effectiveness of the proposed early-warning system, and the results reveal that the proposed early-warning system has superior ability in both accuracy and stability than other benchmark models and can be used as a reliable tool for multi-step ahead air quality early-warning.

Optimal control of novel fuel cell-based DVR using ANFISC-MOSSA to increase FRT capability of DFIG-wind turbine

Although Doubly fed induction generator (DFIG)-based wind turbine has several significant advantages, any voltage fluctuation can severely disturb its normal and safe operation, i.e., it is characteristically very sensitive. Hence, fault ride-through is determined as a prominent benchmark for DFIG which must be enhanced by a voltage compensator. Dynamic voltage restorer (DVR), a voltage fluctuation mitigator, is connected in-series with distribution and sub-transmission lines so that DFIG can appropriately operate during any type of disturbance. In this paper, a new two switches boost converter (TSBC) coupled with quinary multilevel inverter (QMLI) is proposed which is fed by fuel cell to produce staircase sinusoidal voltage for DVR. The proposed step-up DC/DC presents two important advantages: low current ripple and low switch stress, while QMLI can create high-step staircase sinusoidal voltage without considering a number of switches. TSBC-QMLI-based DVR has been controlled by neuro fuzzy inference system controller (ANFISC). As the control problem is multi-objective and complex, Multi-Objective Salp Swarm Algorithm (MOSSA) has been applied to optimally tune the ANFISC’s parameters, and then compared with Non-dominated Sorting Genetic Algorithm II (NSGA-II). To verify and validate the compensation capability of proposed DVR, it has been thoroughly evaluated under different voltage disturbances. Eventually, the simulation results have validated the compensation capability of the suggested TSBC-QMLI-based DVR which is accurately controlled by MOSSA-based ANFISC to mitigate the balanced and unbalanced voltage sag and swell along with the flicker and harmonic voltage.

A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting

Effective and reliable load forecasting is an important basis for power system planning and operation decisions. Its forecasting accuracy directly affects the safety and economy of the operation of the power system. However, attaining the desired point forecasting accuracy has been regarded as a challenge because of the intrinsic complexity and instability of the power load. Considering the difficulties of accurate point forecasting, interval prediction is able to tolerate increased uncertainty and provide more information for practical operation decisions. In this study, a novel hybrid system for short-term load forecasting (STLF) is proposed by integrating a data preprocessing module, a multi-objective optimization module, and an interval prediction module. In this system, the training process is performed by maximizing the coverage probability and by minimizing the forecasting interval width at the same time. To verify the performance of the proposed hybrid system, half-hourly load data are set as illustrative cases and two experiments are carried out in four states with four quarters in Australia. The simulation results verified the superiority of the proposed technique and the effects of the submodules were analyzed by comparing the outcomes with those of benchmark models. Furthermore, it is proved that the proposed hybrid system is valuable in improving power grid management.

Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems

This work proposes two novel optimization algorithms called Salp Swarm Algorithm (SSA) and Multi-objective Salp Swarm Algorithm (MSSA) for solving optimization problems with single and multiple objectives. The main inspiration of SSA and MSSA is the swarming behaviour of salps when navigating and foraging in oceans. These two algorithms are tested on several mathematical optimization functions to observe and confirm their effective behaviours in finding the optimal solutions for optimization problems. The results on the mathematical functions show that the SSA algorithm is able to improve the initial random solutions effectively and converge towards the optimum. The results of MSSA show that this algorithm can approximate Pareto optimal solutions with high convergence and coverage. The paper also considers solving several challenging and computationally expensive engineering design problems (e.g. airfoil design and marine propeller design) using SSA and MSSA. The results of the real case studies demonstrate the merits of the algorithms proposed in solving real-world problems with difficult and unknown search spaces.