Multi-Objective Ant Lion Optimizer Research Articles

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.

Multiobjective Optimization of a Hybrid PV/Wind/Battery/Diesel Generator System Integrated in Microgrid: A Case Study in Djelfa, Algeria

Hybrid Renewable Energy Sources (HRES) integrated into a microgrid (MG) are a cost-effective and convenient solution to supply energy to off-grid and rural areas in developing countries. This research paper focuses on the optimization of an HRES connected to a stand-alone microgrid system consisting of photovoltaics (PV), wind turbines (WT), batteries (BT), diesel generators (DG), and inverters to meet the energy demand of fifteen residential housing units in the city of Djelfa, Algeria. In this context, the multiobjective salp swarm algorithm (MOSSA), which is among the latest nature-inspired metaheuristic algorithms recently introduced for hybrid microgrid system (HMS) optimization, has been proposed in this paper for solving the optimization of an isolated HRES. The proposed multiobjective optimization problem takes into account the cost of energy (COE) and loss of power supply probability (LPSP) as objective functions. The proposed approach is applied to determine three design variables, which are the nominal power of photovoltaic, the number of wind turbines, and the number of battery autonomy days considering higher reliability and minimum COE. In order to perform the optimum size of HMG, MOSSA is combined with a rule-based energy management strategy (EMS). The role of EMS is the coordination of the energy flow between different system components. The effectiveness of using MOSSA in addressing the optimization issue is investigated by comparing its performance with that of the multiobjective dragonfly algorithm (MODA), multiobjective grasshopper optimization algorithm (MOGOA), and multiobjective ant lion optimizer (MOALO). The MATLAB environment is used to simulate HMS. Simulation results confirm that MOSSA achieves the optimum system size as it contributed 0.255 USD/kW h of COE and LPSP of 27.079% compared to MODA, MOGOA, and MOALO. In addition, the optimization results obtained using the proposed method provided a set of design solutions for the HMS, which will help designers select the optimal solution for the HMS.

Multi-objective scheduling of IoT-enabled smart homes for energy management based on Arithmetic Optimization Algorithm: A Node-RED and NodeMCU module-based technique

The home energy management system (HEMS) based on advanced internet of things (IoT) technology has attracted the special attention of engineers in the field of smart grid (SG), which has the task of the demand side management (DSM) and helps to control the equality between demand and electricity supply. The main performance of HEMS is based on the optimal scheduling of home appliances because it manages power consumption by automatically controlling the loads and transferring them from peak hours to off-peak hours. This paper presents a multi-objective version of a newly introduced metaheuristic, called Arithmetic Optimization Algorithm (AOA) to discover optimal scheduling of the home appliances, which is called Multi-Objective Arithmetic Optimization Algorithm (MOAOA). Furthermore, the HEMS architecture has been programmed based on the Raspberry Pi minicomputer with Node-RED and NodeMCU modules. HEMS uses the MOAOA algorithm to find the optimal schedule pattern to reduce daily electricity costs, reduce the peak to average ratio (PAR), and increase user comfort (UC). Real-time pricing (RTP) and critical peak pricing (CPP) signals are presumed as energy tariffs. Simulations are performed in two different scenarios: (I) appliance scheduling scheme and (II) appliance scheduling scheme with the integration of renewable energy sources (RES). The results of MOAOA are compared with Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Gray Wolf Optimizer (MOGWO), and Multi-Objective Antlion optimization (MOALO) algorithms. The results demonstrate that the use of the presented scheme remarkably reduces the cost of electricity consumption as well as PAR, in addition to the integration of MOAOA with RES, which greatly increases user comfort.

Dynamic multiobjective optimization and multivariate analysis for power generation scheduling of the diesel generators in dynamically positioned vessels

Economic and environmental topics together with the higher performance of marine vessels have been becoming more demanding in recent years. This work aims to investigate the optimal operation of diesel generators in the dynamically positioned vessels. Due to the continuous external power demand, the dynamic multiobjective optimization model for optimal scheduling of the diesel generators is generated, taking the fuel consumption and greenhouse gas emission into consideration simultaneously. The present study proposes a 2-phase analysis framework for each time step. A newly developed meta-heuristic optimization algorithm, multi-objective ant lion optimizer (MOALO) is adopted to find the Pareto set in Phase-I. A necessary decision-making procedure based on a simple additive weighting (SAW) approach is utilized to find the final compromise solution in Phase-II. Furthermore, multivariate analysis (MVA) methods are employed to mine the historical features of economic and environmental performances as well as the loadings of all generators. Self-organizing mapping (SOM) together with cluster analysis is utilized to study the relationships among data samples. Multidimensional scaling (MDS) is adopted to examine the relationships among these attributes. An oil rig platform equipped with 8 diesel generators is selected as an illustrative example. The dynamic Pareto fronts and corresponding final compromise solutions obtained from SAW are provided. The effects of various weights upon the history of fuel consumption and emission are examined. The hidden information about the performances in history is studied through SOM and MDS. Results from the numerical example demonstrate that dynamic multiobjective optimization and multivariate analysis extend the application of optimization and data mining in the field of power scheduling for diesel generators in dynamically positioned vessels. The findings of this study add to the understanding of relationships among loads of the generators and corresponding economic and environmental features.

Dynamic optimization of wastewater treatment process based on novel multi-objective ant lion optimization and deep learning algorithm

In this paper, a novel dynamic optimization control based on multi-objective ant lion optimization (DMOALO) and deep learning algorithm is proposed, which could optimize energy consumption (EC) and effluent quality (EQ) simultaneously in the wastewater treatment processes. In order to overcome the difficulty that there is no clear function relationship between the dynamic parameters and the performance indicators, a novel deep belief network (DBN) model for predicting EC and EQ as objective function is proposed. Then, this objective function with constraints is solved by DMOALO method, and the optimal solution would be selected by the intelligent decision system. Finally, Proportional Integral (PI) controllers would be used to track and control these optimal dynamic parameters. DBN-DMOALO-PI optimization control strategy is evaluated in benchmark simulation model 1(BSM1), the simulation results demonstrated this novel optimization control strategy could reduce the EC significantly while meeting the standards of effluent quality parameters. EC is decreased by 3.31% compared with PI optimization control strategy. Therefore, this novel method may reduce the cost of wastewater treatment process effectively, and realize the carbon neutrality in wastewater treatment process.

Short-term Impact Load Forecasting Model Based on Multi-objective Antlion Optimization Algorithm

In this study, a novel multi-objective antlion optimization (MOALO) algorithm-based impact load prediction model is proposed to address forecasting problem in the area with a lot of impact load. To reduce the impact of glitch in impact load data, ensemble empirical mode decomposition(EEMD) is applied to decompose the primitive load data into a set of sub-layers. Then, a novel MOALO-based extreme learning machine (ELM) forecasting model is put forward to make short-term prediction by using the decomposed sub-series. Finally, superimpose the prediction results. According to case study, the proposed EEMD-MOALO-ELM model has the best prediction accuracy and stability.

Optimal Eco-Emission Scheduling of Demand and Supply Side of Microgrids Using Multi-Objective Ant Lion Optimization and Fuzzy Decision-Making

One of the important approaches in demand-side management is shifting energy usage from on-peak time to the other periods considering the financial conditions, which is known as a demand response program. To reduce the environmental concerns, energy policymakers have proposed to use renewable energy resources as clean energy and alternative source to fossil fuels. The wind turbine and photovoltaic panel are used as renewable sources of the microgrid (MG). Demand response programs, including the time of use and critical peak price, are employed by the MG. The profit of the operator of MG from selling the electricity and the produced pollutant gasses of all energy sources are the considered objective functions of the demand-side management problem. The multi-objective ant lion optimizer is used to optimize the indices of the MG and create the optimal Pareto-front. After applying the multi-objective optimization algorithm, the Fuzzy method is used to select the best particle equal to the optimal operational schedule of energy sources and the load pattern of the MG. Ultimately, the proposed method is applied to optimize a sample MG. Results show that the proposed method improves the grid efficiency by optimizing the renewable units and battery.

Exergoeconomic, carbon, and water footprint analyses and optimization of a new solar-driven multigeneration system based on supercritical CO2 cycle and solid oxide steam electrolyzer using various phase change materials

This study presents an innovative multigeneration for power, cooling load, distilled water, and hydrogen production from solar energy. The proposed system is comprised of a supercritical carbon dioxide (sCO2) ejector refrigeration cycle, a solar still desalination unit (SSDU), and a solid oxide steam electrolyzer (SOSE), integrated with parabolic dish collectors (PDCs) field. Exergoeconomic, carbon footprint (CF), and water footprint (WF) analyses are performed to assess the comprehensive performance of the system using seven inorganic and metal high-temperature PCMs, namely MgCl2, NaCl, LiF-MgF2, NaF-CaF2-MgF2, Zn-Cu-Mg, Cu-Si-Mg, and Cu-Si. It is found that Cu-Si delivers superior thermodynamic performance enhancement, and NaF-CaF2-MgF2 leads to the lowest economic, carbon, and water footprint performances among the desired PCMs. Moreover, multi-objective antlion optimization (MOALO) is conducted to ascertain and compare the maximum exergy efficiency and the minimum product cost, CO2 emission, and water consumption rates of Cu-Si and NaF-CaF2-MgF2. Under optimal conditions, Cu-Si gives an exergy efficiency of 31.27% with hydrogen, net power, cooling capacity, and distilled water production of 44.56 kg/h, 1508 kW, 74.03 kW, and 15.48 kg/h, respectively, and NaF-CaF2-MgF2 yields the lowest cost, CO2 emission, and water consumption rates of 73.55 $/h, 86.338 CO2e/h, and 180.73 kg H2O/h, respectively indicating 11.01%, 5.20% and 7.88% improvements with an 8.98% decrement in exergy efficiency compared with Cu-Si.

Multiobjective Ant Lion Optimization for Performance Improvement of Modern Distribution Network

Multiobjective ant lion optimization (MALO) is a technique developed to imitate ant foraging behavior. This method has many advantages, including straightforward, scalable, flexible, balanced, and fast response. The MALO technique consists of five stages: ants perform optimization by random walking by updating their position, building traps, inserting ants into traps, capturing prey, and rebuilding traps. MALO has been successfully used to find optimal solutions to power system problems. Computer-assisted operations characterize modern distribution networks to solve complex problems. The complexity of the distribution network problem is owing to the integration of distributed energy resources (DERs). A DER is a renewable energy power plant with a capacity of up to 10 MW that has gained popularity in recent years. In its application, the integration of DERs into the distribution network can cause new problems, namely load imbalances or excessive voltage increases on the buses where the DER is injected. Therefore, good planning is required to place the DER. This study proposes a multiobjective optimization technique based on MALO to determine the optimal DER location and capacity. MALO is a relatively new optimization method that has the potential to improve distribution network performance. Test cases were conducted for an IEEE 33-bus radial power-distribution network. Four scenarios were considered, integrating DER types I, II, III, and IV. In each design, the placement of one DER, two DERs, and three DERs was modeled to optimize the location and capacity. The results of the multiobjective optimization show that the MALO technique can improve the distribution network performance, which is characterized by a significant power loss reduction, an increase in the bus voltage profile, and a balanced load on each feeder.

Mining and Interpretation of Critical Aspects of Infant Health Status Using Multi-Objective Evolutionary Feature Selection Approaches

The rate of infant mortality (IMR) in a population under one year of age is a marker for infant mortality. It is a major sensitive marker of a community’s overall physical health. Protecting the lives of newborns has become a challenging issue in public health, development programs, and humanitarian initiatives. Almost 10.1% infants died in the United States of America (USA) in 2021. Therefore, this paper aims to extract and understand the various influential factors causing infant deaths in the USA. A crowding distance-based multi-objective ant lion optimization (MOALO-CD) is proposed here with statistical evidence for feature selection. The proposed technique is compared with competitive metaheuristic models such as multi-objective genetic algorithm based on crowding distance (MOGA-CD), multi-objective filter approaches, and recursive feature elimination. Various machine learning classifiers are applied to the selected feature subset obtained from MOALO-CD on the USA’s infant dataset. Extensive experimental results indicate that the proposed model outperforms the existing metaheuristic approaches in terms of Generational Distance, Inverted Generational Distance, Spread, and Hyper volume. Also, the comparative analysis of various machine learning models reveals that random forest achieves significantly better performance on the feature subset obtained from MOALO-CD.

Multiobjective forensic-based investigation algorithm for solving structural design problems

A multi-objective forensic-based investigation (MOFBI) algorithm is developed to solve engineering optimization problems with multiple objectives. In the proposed algorithm, a chaotic map is used to initialize the population; Lévy flight, two elite populations, and a fixed-size archive are used to operate the motions of investigators and police officers in the criminal search and arrest procedures, and a control time mechanism is used to balance exploration and exploitation in MOFBI to obtain Pareto-optimal solutions in multi-objective search spaces. Eight well-known multiple-objective metaheuristic optimization algorithms - the multi-objective ant lion optimizer (MOALO), the multi-objective dragonfly algorithm (MODA), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), the multi-objective grey wolf optimizer (MOGWO), the multi-objective particle swarm optimization (MOPSO), the fast and elitist multi-objective genetic algorithm (NSGA-II), pareto envelope-based selection algorithm II (PESA-II) and the strength pareto evolutionary algorithm II (SPEA-II) - are compared to MOFBI with respect to performance in solving 24 multi-objective mathematical benchmark problems (CEC-2020 functions). The Wilcoxon rank sum test of hypervolume index, generational distance and spacing reveal that MOFBI can find more accurate approximations of Pareto-optimal fronts than the other algorithms. MOFBI is then used to solve five structural engineering design problems, including the 10-bar truss, the 72-bar truss, the 56-bar dome, the 120-bar dome and the 582-bar tower. The results obtained using MOFBI and comparison thereof with previously obtained results indicate the effectiveness of MOFBI in finding the best Pareto-optimal solutions. Therefore, MOFBI is a powerful computer-aided tool for solving multi-objective optimization problems.

Pareto optimum design of an adaptive robust backstepping controller for an unmanned aerial vehicle

AbstractThis research develops an adaptive robust backstepping (AR‐backstepping) controller for stabilization of an unmanned aerial vehicle (UAV) having strong coupling and highly nonlinear dynamics. At first, the backstepping control method as the basic stabilizer is utilized to determine the control efforts of the considered UAV. Next, an adaptive‐robust mechanism subject to gradient decent methods and sliding surfaces is implemented to regulate the control gains. In fact, this mechanism determines the speed of changes of the gain values of the backstepping controller to make better responses in the presence of disturbances and uncertainties. Then, the optimum values of the design parameters related to the adaptive‐robust mechanism are selected by using a multi‐objective ant‐lion optimization (MOALO) algorithm to simultaneously minimize the total error and control efforts. Finally, the results for a UAV developed in the Sirjan University of Technology, Sirjan, Iran, are given to confirm the effectiveness, robustness, and advantages of the designed AR‐backstepping controller.

Research of a combined wind speed model based on multi‐objective ant lion optimization algorithm

In the past few decades, wind power generation has gradually become an important source in the global energy market, which effectively meets the energy demand of human production activities. However, the instability and diffusion of wind speed bring difficulties to the wind energy development and promotion. For improving the predictive accuracy of original sequence, scholars have proposed a variety of prediction models, but many current forecasting models often neglect the importance of data processing and can be easily limited by a single model, which causes poor performances. Therefore, a combined model is built that mainly includes the complete ensemble empirical mode decomposition with adaptive noise, several single models, and multi-objective ant lion optimization algorithm. This combined model not only reduces the impact of high-frequency noise, but also extracts original sequences features as much as possible, combines the advantages of multiple single models, and greatly improves forecasting accuracy and stability.

Time and Energy Minimization Communications Based on Collaborative Beamforming for UAV Networks: A Multi-Objective Optimization Method

Unmanned aerial vehicle (UAV) communications and networks are of utmost concern. However, they have challenges such as the limited on-board energy and restricted transmit power. In this paper, we study a UAV-enabled communication scenario that a set of UAVs perform a virtual antenna array (VAA) to communicate with different remote base stations (BSs) by using collaborative beamforming (CB). To achieve a better transmission performance, the UAV elements can fly to optimal positions by using optimal speeds and adjust to optimal excitation current weights for performing CB transmissions. However, there are some trade-offs between energy consumption and transmission performance. Thus, we formulate a time and energy minimization communication multi-objective optimization problem (TEMCMOP) of CB in UAV networks to simultaneously minimize the total transmission time, total performing time of VAAs and total motion and hovering energy consumptions of UAVs by jointly optimizing the positions, flight speeds and excitation current weights of UAVs, as well as the order of communicating with different BSs. Due to the complexity and NP-hardness of the formulated TEMCMOP, we propose an improved multi-objective ant lion optimization (IMOALO) algorithm with chaos-opposition based learning solution initialization and hybrid solution update operators to solve the problem. Simulation results verify that the proposed IMOALO can effectively solve the formulated TEMCMOP and it has better performance than some other benchmark approaches.

A new optimization algorithm to solve multi-objective problems

Simultaneous optimization of several competing objectives requires increasing the capability of optimization algorithms. This paper proposes the multi-objective moth swarm algorithm, for the first time, to solve various multi-objective problems. In the proposed algorithm, a new definition for pathfinder moths and moonlight was proposed to enhance the synchronization capability as well as to maintain a good spread of non-dominated solutions. In addition, the crowding-distance mechanism was employed to select the most efficient solutions within the population. This mechanism indicates the distribution of non-dominated solutions around a particular non-dominated solution. Accordingly, a set of non-dominated solutions obtained by the proposed multi-objective algorithm is kept in an archive to be used later for improving its exploratory capability. The capability of the proposed MOMSA was investigated by a set of multi-objective benchmark problems having 7 to 30 dimensions. The results were compared with three well-known meta-heuristics of multi-objective evolutionary algorithm based on decomposition (MOEA/D), Pareto envelope-based selection algorithm II (PESA-II), and multi-objective ant lion optimizer (MOALO). Four metrics of generational distance (GD), spacing (S), spread (Δ), and maximum spread (MS) were employed for comparison purposes. The qualitative and quantitative results indicated the superior performance and the higher capability of the proposed MOMSA algorithm over the other algorithms. The MOMSA algorithm with the average values of CPU time = 2771 s, GD = 0.138, S = 0.063, Δ = 1.053, and MS = 0.878 proved to be a robust and reliable model for multi-objective optimization.

Comparison of NSGA-II, MOALO and MODA for Multi-Objective Optimization of Micro-Machining Processes.

The popularity of micro-machining is rapidly increasing due to the growing demands for miniature products. Among different micro-machining approaches, micro-turning and micro-milling are widely used in the manufacturing industry. The various cutting parameters of micro-turning and micro-milling has a significant effect on the machining performance. Thus, it is essential that the cutting parameters are optimized to obtain the most from the machining process. However, it is often seen that many machining objectives have conflicting parameter settings. For example, generally, a high material removal rate (MRR) is accompanied by high surface roughness (SR). In this paper, metaheuristic multi-objective optimization algorithms are utilized to generate Pareto optimal solutions for micro-turning and micro-milling applications. A comparative study is carried out to assess the performance of non-dominated sorting genetic algorithm II (NSGA-II), multi-objective ant lion optimization (MOALO) and multi-objective dragonfly optimization (MODA) in micro-machining applications. The complex proportional assessment (COPRAS) method is used to compare the NSGA-II, MOALO and MODA generated Pareto solutions.

Multi-Objective Ant Lion Optimizer for Stochastic Robotic Disassembly Line Balancing Problem Subject to Resource Constraints

With the development of manufacturing and society, a large amount of end-of-life products are generated but with reuse value. Therefore, it is of great significance to dismantle and reuse them. A robotic linear disassembly line plays an important role and promotes the disassembly efficiency in a disassembly process. In this work, we establish a mathematical model of a linear robotic disassembly line balancing problem, which requires the maximization of profit and minimization of energy consumption. Then, an improved ant lion optimizer is proposed to optimize the objectives of the multi-objective robotic disassembly line balancing problem. The experimental results show that the multi-objective ant lion optimizer has good performance, and the diversity and convergence are better than its peers.