Multi-Objective Ant Lion Optimizer Research Articles

A health indicator enabling both first predicting time detection and remaining useful life prediction: Application to rotating machinery

Remaining Useful Life (RUL) prediction is vital for system functionality. Non-end-to-end approaches is an important type of RUL prediction approaches for their important application in industrial scenarios. In non-end-to-end approaches, Health Indicator (HI) construction is a critical aspect. However, existing HI construction approaches ignore First Predicting Time (FPT) detection, leading to increased domain knowledge demand and system health comprehension difficulty. To address this issue, this paper proposes a multi-objective-optimization-based HI construction approach enabling both FPT detection and RUL prediction. A novel metric called the monotonicity strength index is proposed to address the limitation of the conventional monotonicity. The constructed HI possesses the ability to indicate FPT by taking the detectability metric as an optimization objective. The optimization problem is solved by the combination of the multi-objective ant lion optimizer and the entropy weight method. The superiority of this HI is demonstrated through experiments on the widely used IMS bearing dataset and a gearbox dataset.

Multi-Objective Optimization of Cell Voltage Based on a Comprehensive Index Evaluation Model in the Aluminum Electrolysis Process

In the abnormal situation of an aluminum electrolysis cell, the setting of cell voltage is mainly based on manual experience. To obtain a smaller cell voltage and optimize the operating parameters, a multi-objective optimization method for cell voltage based on a comprehensive index evaluation model is proposed. Firstly, a comprehensive judgment model of the cell state based on the energy balance, material balance, and stability of the aluminum electrolysis process is established. Secondly, a fuzzy neural network (FNN) based on the autoregressive moving average (ARMA) model is designed to establish the cell-state prediction model in order to finish the real-time monitoring of the process. Thirdly, the optimization goal of the process is summarized as having been met when the difference between the average cell voltage and the target value reaches the minimum, and the condition of the cell is excellent. And then, the optimization setting model of cell voltage is established under the constraints of the production and operation requirements. Finally, a multi-objective antlion optimization algorithm (MOALO) is used to solve the above model and find a group of optimized values of the electrolysis cell, which is used to realize the optimization control of the cell state. By using actual production data, the above method is validated to be effective. Moreover, optimized operating parameters are used to verify the prediction model of cell voltage, and the cell state is just excellent. The method is also applied to realize the optimization control of the process. It is of guiding significance for stabilizing the electrolytic aluminum production and achieving energy saving and consumption reduction.

Hunting-Based Optimization Technique for Secured Optimal Power Flow with Lines Outages in Power System

This paper aims to achieve the exact resolution of an optimal power flow (OPF) problem in an electrical network. In the OPF, the goal is to plan the production and distribution of electrical power flows to cover, at minimal fuel cost, the consumption at various points in the network. Three variants of the OPF problem are studied in this manuscript. The first one, OPF corresponds to the case where power production costs in the network are modeled with a quadratic cost. In the second variant, OPF with outages of some lines, we clarify the extent to which power flow is affected by the outages and the increasing number of overloaded lines. Finally, the last variant, secured OPF corresponds to the case where the management of production units can respect the power limit of each line by rescheduling power production units. The study focuses on congestion management in the IEEE 30 bus system by applying a model for OPF, incorporating data from both transmission lines and generators. The research proposes a Hunting Optimization Technique which is named “Multi-Objective Ant Lion Optimizer (MOALO)” to solve single and multi-objective optimization problems to find a solution for management pricing, comparing results with other research methods to show the effectiveness of the applied approach and the mathematical model representing congestion management.

Optimizing Grid-Connected Photovoltaic (PV) Battery Energy Storage through Multi-Objective Ant-Lion Optimization (MOALO)

Optimizing Grid-Connected Photovoltaic (PV) Battery Energy Storage through Multi-Objective Ant-Lion Optimization (MOALO)

MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

Multi-objective ant lion optimization for parameter optimization of wireless power transfer systems

The effective transmission distance of a magnetic coupling resonance (MCR) wireless power transfer (WPT) system is an essential index of wireless power transfer. Existing systems often suffer from short transmission distances, low power, and low efficiency. In this paper, a method is proposed for improving the parameters of wireless power transfer systems to enhance the performance of the systems. The main parameters affecting the output power and transmission efficiency are determined by analyzing the MCR WPT system with SS-type topology. In addition, the multi-objective ant lion optimizer is combined with tent chaotic mapping to generate the location information of the initial population by using the distribution and randomness of tent chaotic mapping, which increases the diversity of the people and improves the convergence speed and global search capability of the algorithm to achieve multi-objective parameter optimization, and the optimized model is also analyzed. Experiments show that the optimized MCR WPT system has improved output power and transmission efficiency at a longer distance. The optimal transmission distance of the system is about 0.2 m with a maximum output power of 129.3 W, and the axial offset stability of the system is enhanced. Finally, the effectiveness of the improved model is verified by building a prototype system. It provides a valuable reference for the research of improving wireless power transfer.

Multi-Objective Optimization in Solar and Wireless Energy Harvesting the IoT Relay Communication System

In this article, a solar and wireless energy harvesting (SWEH) Internet of Things network is presented. To address energy efficiency problems, we consider an amplify-and-forward relay node that harvests solar and wireless energy using the power splitting protocol to accomplish SWIPT. For the communication model under consideration, we derive the mathematical expressions for throughput and energy efficiency. We investigate the impact of both power transmitted by the source and power-splitting factor on the system’s achievable energy efficiency and the energy harvested. To maximize the contradictory objectives of energy efficiency and energy harvested simultaneously, we propose to use the Pareto-based multi-objective ant lion optimization algorithm (MOALO) and jointly optimize the transmitted power and power-splitting factor. In-depth simulation is used to determine the Pareto optimal values for transmitted power and power-splitting factor and to assess how the proposed algorithm performs in comparison to leading-edge algorithms such as NSGA-II, MOPSO, MOWOA, and MOMVO. The results unambiguously show that the suggested MOALO offers the best Pareto front within a reasonable amount of time. Finally, the results corroborate that the proposed SWEH system outperforms in terms of harvested energy and attainable energy efficiency.

Combined wind speed forecasting model based on secondary decomposition and quantile regression closed-form continuous-time neural network

ABSTRACT Reliable interval forecasts can quantify the potential risk of wind speeds, which can help people make better use of wind energy. In this study, a new probabilistic prediction model, called Quantile Regression Closed-Form Continuous-time Neural Network (QRCfC), is proposed by combining quantile regression and closed-form continuous-time neural network. A new combined model combining QRCfC, secondary decomposition, multi-objective optimization, and dynamic weight combination strategy is proposed, which makes full use of the advantages of each single model to obtain accurate deterministic prediction and reliable probabilistic interval prediction of wind speed. First, the original wind speed series is divided into several subseries using a secondary decomposition technique built on variational mode decomposition and singular spectrum analysis. Then, four base models are used to predict these subseries separately. After that, the predicted values of the four base models are input into QRCfC for training, where the hyperparameters of QRCfC are dynamically adjusted by a multi-objective ant lion optimization algorithm. Finally, to verify the effectiveness of the proposed models, experiments are conducted using data sets from three wind farms in Gansu, China. Simulation results show that the proposed model achieves excellent prediction performance in both deterministic and probabilistic prediction. For example, compared with the unoptimized quantile regression time convolution network, quantile regression long-short time memory, quantile regression gated recurrent unit, and quantile regression neural network, the proposed model reduces the MAPE by 21.89% and the CRPS by 11.14% for 1-step prediction at site 1.

MOIMPA: multi-objective improved marine predators algorithm for solving multi-objective optimization problems

This paper introduces a multi-objective variant of the marine predators algorithm (MPA) called the multi-objective improved marine predators algorithm (MOIMPA), which incorporates concepts from Quantum theory. By leveraging Quantum theory, the MOIMPA aims to enhance the MPA’s ability to balance between exploration and exploitation and find optimal solutions. The algorithm utilizes a concept inspired by the Schrödinger wave function to determine the position of particles in the search space. This modification improves both exploration and exploitation, resulting in enhanced performance. Additionally, the proposed MOIMPA incorporates the Pareto dominance mechanism. It stores non-dominated Pareto optimal solutions in a repository and employs a roulette wheel strategy to select solutions from the repository, considering their coverage. To evaluate the effectiveness and efficiency of MOIMPA, tests are conducted on various benchmark functions, including ZDT and DTLZ, as well as using the evolutionary computation 2009 (CEC’09) test suite. The algorithm is also evaluated on engineering design problems. A comparison is made between the proposed multi-objective approach and other well-known evolutionary optimization methods, such as MOMPA, multi-objective ant lion optimizer, and multi-objective multi-verse optimization. The statistical results demonstrate the robustness of the MOIMPA approach, as measured by metrics like inverted generational distance, generalized distance, spacing, and delta. Furthermore, qualitative experimental results confirm that MOIMPA provides highly accurate approximations of the true Pareto fronts.

Coverage Enhancement Strategy for WSNs Based on Multiobjective Ant Lion Optimizer

In order to improve the quality of service and prolong the network lifetime of wireless sensor networks (WSNs), optimizing the network coverage rate and the sensor nodes moving distance in the secondary deployment process has become a crucial research object. Aiming at these two goals, this paper improves the classic multi-objective ant lion optimization algorithm, and proposes an improved multi-objective ant lion optimization algorithm based on fast non-dominated sorting (NSIMOALO). Firstly, we use the idea of fast non-dominated sorting algorithm and elite strategy of NSGA-II, which avoids the algorithm from falling into the local optimal solution and improves the solution accuracy of the algorithm. Secondly, a more reasonable congestion calculation equation is proposed, which greatly increases the diversity of the population. Finally, we introduce Lévy flight to update the ant position by dynamically changing the weight coefficients among the antlion, elite antlion and Lévy flight, which improves the global optimization ability of the algorithm. The standard function simulation results show that NSIMOALO algorithm has higher convergence and coverage. The algorithm is applied to WSNs sensor nodes deployment, and 80 sensor nodes are deployed in a monitoring area of 200m×300m. Compared with MOALO algorithm, NSGA-II and NSMOFPA, the coverage rate of NSIMOALO algorithm is increased by 12.753%, 12.413% and 4.492% respectively, and the sensor nodes average moving distance is decreased by 2.551m, 2.316m and 4.457m respectively.

Optimal reservoir operation using the improved multi-step-ahead time-varying hedging rule under climate and land-use changes

ABSTRACT This study aims to optimize Yeywa Hydropower Reservoir (YHR) operation using the multi-step-ahead time-varying hedging (TVH) rule under climate change (CC) and land-use change (LUC) to improve summer power generation. The performance of three multi-objective algorithms – the Multi-objective Salp Swarm Algorithm (MOSSA), the Multi-objective Antlion Optimizer (MOALO) and the Non-dominated Sorting Whale Optimization Algorithm (NSOWA) – are compared. MOSSA provides the best solutions with a higher mean hypervolume in a shorter computation time, and it is utilized to optimize the TVH rules for four periods: monthly (TVH-1), quarterly (TVH-3), half-yearly (TVH-6) and yearly (TVH-12). The six-month-ahead TVH-6 rule and the five-month-ahead TVH-6 rule generate the highest summer power for the historical period (2011–2020) and the future period (2020–2059), respectively. The future decadal power generation is expected to be higher than the historical power generation. The future TVH-6 rule is more reliable and it has lower water deficits than the historical YHR operation rule.

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In this research paper, a swarm evolutionary modeling technique called the Updated Multi-Objective Ant-Lion Optimizer (UMO-ALO) is applied to the modeling of Open Circuit Voltage (OCV) state-of-charge (SOC) of Lithium-ion cells with cathode-anode composition (LiMnO2/Li4TiO). The model approximates the battery SOC by considering several battery cell internal physical parameters and a linear fitness function governed by two objectives involving the charge and discharge response of the battery cell. Bound limited coefficients of the physical parameters are used in the optimization process. The UMO-ALO uses a special update procedure to reduce the computational run-time of standard MO-ALO and hence speeds up the generic optimizer. Simulations were performed using a very small real laboratory data obtained from the relevant field studies and for different trial-run configurations of UMO-ALO. The results from these simulations show very good fitness responses close to zero margins for both trial-run configurations.

A novel MOALO-MODA ensemble approach for multi-objective optimization of machining parameters for metal matrix composites

Machining of metal matrix composites (MMCs) has now become an essential task in shaping them to their final usable forms for various industrial applications. These machining operations may range from drilling of simple through holes to cutting and shaping of the composites into complex shapes. Determination of the optimal process parameters during their machining presents a combinatorial optimization problem, which may turn out to be more complex due to involvement of various material dependent parameters of the MMCs, like particle size, reinforcement percentage etc. The importance of optimization increases manifold due to conflicting settings of different process parameters for attaining the desired quality characteristics. In this paper, two nature-inspired optimization algorithms, i.e. multi-objective antlion optimization (MOALO) and multi-objective dragonfly algorithm (MODA) are applied for optimization of various process parameters during machining of MMCs. To mitigate uncertainties in global optimality of the predicted Pareto optimal fronts arising due to stochastic nature of the metaheuristics, an ensemble approach integrating MOALO and MODA techniques is proposed here. It is demonstrated with the help of two case studies that MOALO-MODA ensemble is far superior to its individual counterparts and thus, can be employed for development of robust and reliable Pareto optimal fronts.

A multi-objective optimization model for green demand responsive airport shuttle scheduling with a stop location problem

<abstract> <p>We proposed a multi-objective optimization framework for green demand responsive airport shuttle scheduling, which simultaneously aims at assigning demand points to selected stops and routing airport shuttles to visit these stops in their overlapping time windows to transport all passengers from their home or workplaces to the airport. Our objectives were to minimize total travel time for passengers, the punishment expense of violating the time-window as well as carbon emissions for all shuttles. Since such issues belongs to the NP-problem, a two-stage Multi-objective ant lion optimizer (MOALO)-based algorithm incorporating dynamic programming search method was developed to acquire the optimal scheduling schemes. Finally, a case study of airport shuttle service in Tianjin Airport, China, was used to demonstrate the validity of the model and algorithm.</p> </abstract>

Nodal Electricity Price Based Optimal Size and Location of DGs in Electrical Distribution Networks Using ANT LION Optimization Algorithm

Distribution system has been the weakest link in the entire power system supply chain. It is also one of the most vital parts of the power system. However, a lot of methods have been developed to improve the condition of the distribution system. The use of distributed generations (DGs) is one such method where the generated power is closer to the load center, and the DG is also providing ancillary services to the grid. The nodal electricity price for DGs location is determined based on the Locational Marginal Price (LMP). LMP implies the price to buy and sell power at each node within electrical distribution markets. In the nodal electricity market (EM), the cost of energy is determined by the location of DG to which it is provided. This paper presents a novel approach that utilizes nodal electricity price for optimal sizing and location (OSL) of DGs. A multi-objective ANTLION optimization (MOALO) has been utilized as an optimization approach to compute the OSL of DGs units. ANTLION optimization (ALO) is based on the unique hunting behaviour of antlions. Optimization has been done for social welfare maximization, loss minimization, and voltage profile improvement in distribution networks (DNs). The results of the proposed technique have been evaluated for IEEE 33 bus DNs.

Efficient multi-objective meta-heuristic algorithms for energy-aware non-permutation flow-shop scheduling problem

This study investigates the optimization of non-permutation flow-shop scheduling problems and lot-sizing simultaneously. Contrary to previous works, we first study the energy awareness of non-permutation flow-shop scheduling and lot-sizing using modified novel meta-heuristic algorithms. In this regard, first, a mixed-integer linear mathematical model is proposed. This model aimed to determine the size of each sub-category and determine each machine's speed within each sub-category to minimize makespan and total consumed energy simultaneously. In order to optimize this model, Multi-objective Ant Lion Optimizer (MOALO), Multi-objective Keshtel Algorithm (MOKA), and Multi-objective Keshtel and Social Engineering Optimizer (MOKSEA) are proposed. First, the validation of the mathematical model is evaluated by implementing it in a real case of the food industry using GAMS software. Next, the Taguchi design of the experiment is applied to adjust the meta-heuristic algorithms' parameters. Then the efficiency of these meta-heuristic algorithms is evaluated by comparing with Epsilon-constraint (EPC), Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO) using several test problems. The results demonstrated that the MOALO, MOKA, and MOKSEO algorithms could find optimal solutions that can be viewed as a set of Pareto solutions, which means the used algorithm has the necessary validity. Moreover, the proposed hybrid algorithm can provide Pareto solutions in a shorter time than EPC and higher quality than NSGA-II and MOPSO. Finally, the model's key parameters were the subject of sensitivity analysis; the results showed a linear relationship between the processing time and the first and second objective functions.

MSSA-DEED: A Multi-Objective Salp Swarm Algorithm for Solving Dynamic Economic Emission Dispatch Problems

Due to the rising cost of fuel, increased demand for energy, and the stresses of environmental issues, dynamic economic emission dispatch (DEED), which is the most precise mode for actual dispatching conditions, has been a significant study topic in current years. In this article, the higher dimensional, deeply correlated, non-convex, and non-linear multi-objective DEED problem is designated, involving both the fuel costs and emissions objectives simultaneously. In addition, the valve point effect, transmission loss, as well as the ramping rate, are considered. The Salp Swarm Algorithm (SSA) is a well-established meta-heuristic that was inspired by the foraging behavior of salps in deep oceans and has proved to be beneficial in estimating the global optima for many optimization problems. The objective of this article is to evaluate the performance of the multi-objective Salp Swarm Algorithm (MSSA) for obtaining the optimal dispatching schemes. Furthermore, the fuzzy decision-making (FDM) approach is employed to achieve the best compromise solution (BCS). In order to confirm the efficacy of the MSSA, the IEEE 30-bus six-unit power system, standard 39-bus ten-unit New England power system, and IEEE 118-bus fourteen-unit power system were considered as three studied cases. The obtained results proved the strength and supremacy of the MSSA compared with two well-known algorithms, the multi-objective grasshopper optimization algorithm (MOGOA) and the multi-objective ant lion optimizer (MALO), and other reported methods. The BCS of the proposed MSSA for the six-unit power system was USD 25,727.57 and 5.94564 Ib, while the BCS was 2.520778 × USD 106 and 3.05994 × 105 lb for the ten-unit power system, and was 1.29200 × USD106 and 98.1415 Ib for the 14 generating units. Comparisons with the other well-known methods revealed the superiority of the proposed MSSA and confirmed its potential for solving other power systems’ multi-objective optimization problems.

Hybrid multi-objective Harris Hawk optimization algorithm based on elite non-dominated sorting and grid index mechanism

In order to find Pareto optimal solution set uniformly distributed along all objectives, a Hybrid Multi-Objective Harris Hawk Optimization Algorithm (H-MOHHO) was proposed based on elite non-dominated sorting and grid indexing mechanism. In order to maintain and improve the coverage of Pareto optimal solution, a method combining the two terms is adopted to obtain the optimal Pareto optimal solution set. Firstly, a non-dominated ranking mechanism based on elite was used to assign rank and sum to select the best solution set, and then the archived grid index mechanism with update mechanism was used to select the final solution set. This hybrid structure can not only obtain the optimal Pareto solution set but also keep the diversity of the population and improve the effectiveness of solving multi-objective optimization problems. In order to verify the performance of the proposed H-MOHHO algorithm, 22 test functions and 4 multi-objective engineering problems are used for simulation, and four performance indexes are compared with Multi-Objective Particle Swarm Optimization (MOPSO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Ant Lion Optimizer (MOALO), Multi-Objective Salp Swarm Algorithm (MSSA) and Multi-Objective Dragonfly Algorithm (MODA). Experimental results show that the proposed H-MOHHO algorithm has better competitiveness and applicability.

Research on Multiobjective Optimization Design of Meshing Performance and Dynamic Characteristics of Herringbone Gear Pair Under 3D Modification

Abstract Herringbone gear transmission system is widely used in high-speed overloaded fields such as ships. Tooth deformation and installation error, which can cause meshing impact, load mutation, and load uneven, will seriously affect dynamic performance of the whole transmission system. Modification technology is the most effective way to achieve damping and noise reduction. In this article, we propose a three-dimensional (3D) modified tooth surface by grinding wheel along axial to the spiral rise and along radial to the parabolic movement based on the grinding principle and a multiobjective ant lion optimizer model for optimizing meshing performance and dynamic characteristics of herringbone gear transmission system and analyze the impact of optimized modifications of tooth profile, axial, and three dimension on meshing performance, loaded transmission error, load distribution coefficient and meshing impact, meshing stiffness, and vibration acceleration by the example. The results show that the 3D modification of optimization can not only eliminate the contact between the tooth side edge and tooth top edge but also eliminate the influence of installation error on contact performance. The root mean square values of the relative comprehensive vibration acceleration of tooth profile modification, axial modification, and 3D modification are reduced by 30.11%, 49.24%, and 61.41% compared with the standard, respectively. The 3D modification can greatly reduce tooth vibration, reduce resonance peak, and achieve the goal of noise reduction.

A Novel Metaheuristic Optimization for Throughput Maximization in Energy Harvesting Cognitive Radio Network

In this article, a novel technique is proposed, namely rank-based multi-objective antlion optimization (RMOALO), and applied to optimize the performance of the energy harvesting cognitive radio network (EHCRN). The original selection method in multi-objective antlion optimizer (MOALO) is suitably changed to improve the algorithm, thus reaching the optimal solution for the problem. The proposed technique shows considerable performance improvement over the method used in the multi-objective antlion optimizer (MOALO). The performance of the proposed RMOALO is demonstrated on five benchmark mathematical functions and compared to multi-objective particle swarm optimization (MOPSO), multi-objective moth flame optimization (MOMFO), MOALO-Tournament, and MOALO-Roulette. The simulation results show an improved convergence of RMOALO and find the optimal solution to the throughput maximization problem. We show that RMOALO provides 16.33 % improved average throughput with the optimal value of sensing duration for the varying amount of harvested energy compared to MOPSO, MOMFO, MOALO-Roulette, and MOALO-Tournament.