Multi-objective Search Algorithm Research Articles

Chemotherapy Regimen Optimization Using a Two-Archive Multi-Objective Squirrel Search Algorithm

Chemotherapy is one of the most effective treatments for cancer, but the efficacy of standard chemotherapy regimens is often limited by toxicities and the individual heterogeneity of cancers. Precise dosing is an important tool to improve efficacy and reduce significant differences in toxicity. However, most of the existing studies on chemotherapy optimization fail to fully consider the toxic side effects, drug resistance, and drug combinations, and thus the chemotherapy regimens obtained may face difficulty in achieving the expected efficacy and also affect the subsequent treatment. Therefore, this paper establishes a tumor growth model for the combination chemotherapy of cell cycle-specific and non-cycle-specific drugs and includes the factors of acquired drug resistance and toxic side effects, proposing an improved multi-objective Squirrel Search Algorithm, the TA-MOSSA, to solve the problem of accurate chemotherapy drug optimization. In this paper, experiments were conducted to analyze the efficacy of chemotherapy dosing regimens obtained by the TA-MOSSA based on the tumor growth model, and the results show that the TA-MOSSA can provide effective chemotherapy regimens for patients who take different treatment approaches.

Construction of new energy consumption optimization model based on improved pathfinder algorithm

As traditional energy reserves continue to decline, the importance of new energy sources increases. However, the current traditional power system often fails to consider new energy sources, particularly in power supply systems that integrate multiple new energy sources. The cost, efficiency, and environmental factors seriously affect the energy system’s efficiency. Therefore, this proposal presents a multi-objective optimization discrete assignment pathfinder algorithm. The algorithm can handle multi-objective optimization problems and adapt to various constraints, providing a more precise optimization scheme for new energy systems. The experimental results indicated that the proposed research method exhibits better performance compared to other algorithms of the same type. Compared with the multi-objective multivariate universe optimization algorithm and the multi-objective sparrow search algorithm, the research method was ahead in terms of fitness value by 9.54% and 14.67%, respectively. Meanwhile, in the grid simulation, the research method achieved an average efficiency of 96.16%, which is better than the comparative algorithms by 6.57–14.02%. The study not only improves the optimization efficiency of new energy consumption, but also provides a powerful decision support tool for the planning and operation of wind farms. It is of great significance for the improvement of power system efficiency and decarbonization, and helps to promote the large-scale integration and sustainable development of new energy.

An efficient multi-objective adaptive large neighborhood search algorithm for solving a disassembly line balancing model considering idle rate, smoothness, labor cost, and energy consumption

The concept of green manufacturing emphasizes the importance of product disassembly in achieving energy-efficient recycling and remanufacturing operations. Disassembly line balancing (DLB) is a critical component of the product disassembly process, wherein a set of tasks must be allocated to workstations for disassembly. This study proposes a multi-objective DLB model that aims to minimize multiple conflicting objectives simultaneously including idle rate, smoothness, labor cost, and energy consumption. A key innovation of this study involves the creation of a tailored adaptive large neighborhood search (ALNS) algorithm which is one of the first studies in the literature on product disassembly algorithms. The developed ALNS employs efficient construction and destruction heuristics to solve the proposed multi-objective DLB problem. The ALNS algorithm aims to destroy and repair solutions effectively, while a local search procedure helps it escape from local optimum solutions. The provided DLB model is effectively solved by applying the proposed ALNS algorithm to the disassembly process of a turbine reducer. The obtained results from this application serve as a compelling demonstration of the efficiency and effectiveness of the proposed approach. Furthermore, comparisons conducted with various state-of-the-art algorithms using small and large instance sets consistently highlight the superiority of the proposed ALNS approach.

Simulation-driven fault detection for the gear transmission system in major equipment

Scholars and engineers attach great importance to fault detection in mechanical systems due to the unpredictable faults that arise from long-term operations under complex and extreme conditions. The fact that each type of fault embodies unique characteristics makes it challenging to obtain sufficient fault samples, and conventional machine learning methods fail to provide satisfactory fault diagnosis results. To address this issue, a simulation-driven fault detection method has been proposed in this paper. Firstly, the DT model of the gear transmission system was established. An improved multi-objective sparrow search algorithm (MOSSA) was employed to update the model and obtain an adequate number of simulation fault samples as well. Secondly, a two-stage adversarial domain adaptation model with full-scale feature fusion (ADAM-FF) was utilized to align and integrate the features of simulated and generated fault samples. This enables model training and classification of combined samples, facilitating the detection of unknown faults in actual measurements. Lastly, a simulation-driven equipment health index assessment model which accurately and non-destructively evaluates the degradation status of the equipment was introduced. This model effectively quantifies the extent of equipment degradation, thereby facilitating the transfer from the simulation realm to practical engineering applications. To validate the effectiveness of the proposed fault detection method, an experimental study was conducted on the extruder gear reducer of a petrochemical enterprise. The proposed fault detection method has the potential for widespread application across a range of large-scale mechanical equipment. As such, the utilization of this method will enable proactive maintenance planning, ensure safe and stable equipment operations, and minimize energy loss.

A multi-objective crow search algorithm for optimizing makespan and costs in scientific cloud workflows (CSAMOMC)

A multi-objective crow search algorithm for optimizing makespan and costs in scientific cloud workflows (CSAMOMC)

MORSA: Multi-objective reptile search algorithm based on elite non-dominated sorting and grid indexing mechanism for wind farm layout optimization problem

The proposal of the wind farm layout optimization (WFLO) problem aims to better utilize wind energy. A multi-objective reptile search algorithm (MORSA) based on elite non-dominated sorting and grid indexing mechanism was proposed to solve the multi-objective optimization problem of wind farm layout under the Jansen wake model to maximize power generation while minimizing costs. Firstly, the elite non-dominated sorting method was used to sort the population, and then the crowding distance method was used to maintain the diversity between the optimal sets. By calculating the crowding distance, the same level of non-dominated populations are sorted. Then, the grid index mechanism is added to the archive for selection and deletion. Leaders can use the roulette wheel to select and delete some solutions in high-density grids. By obtaining the optimal Pareto solution set while maintaining the diversity of the population, the effectiveness of solving multi-objective optimization problems is improved. The improved algorithm solves the WFLO problem based on the Jansen wake model by using two different initial self-flow winds for performance testing while considering the effects of single wake and multiple wake with or without partial wake occlusion. The performance of the WFLO (output power and cost) was compared without considering wake obstruction, MORSA can obtain the minimum turbine cost of 26.3601 and the maximum power generation of 2.3078E-08 with a single wake flow of 16 m/s. The experimental results show that the proposed MORSA has better convergence and applicability, and has shown good results in multi-objective layout optimization of wind farms.

A Framework for Valuation and Portfolio Optimization of Venture Capital Deals with Contractual Terms

Venture capitalists invest not only in the business aspect of a deal but also in its contractual terms. Therefore, the selection of deals and the combination of contractual terms pose challenging decisions for them. This paper consists of two main sections. The first section introduces a novel framework for the valuation of venture capital (VC) deals, including startups and their contractual terms. By taking into account risk situations, this section presents the valuation of combined contractual terms, including call options, liquidity preference, and participant rights. In the second section, a new multiobjective mathematical model for VC deals and contractual terms portfolio selection is developed using right-tail probability, strategy alignment, and a utility function. To solve the proposed model, three metaheuristic algorithms—Non-Dominated Sorting Genetic Algorithm (NSGA-II), Multi-Objective Binary Harmony Search Algorithm, and Dynamic Tuning Parameter Binary Harmony Search Algorithm (DTPBHS)—are applied. Based on numerical examples, DTPBHS outperforms other algorithms in the “Mean Ideal Distance” index, but NSGA-II demonstrates the best performance in the “Rate of Achievement of two objectives simultaneously” index. Furthermore, we demonstrate that the proposed utility function is more robust than the right-tail probability function under default deals conditions.

A Multi-Objective Trajectory Planning Method of the Dual-Arm Robot for Cabin Docking Based on the Modified Cuckoo Search Algorithm

During the assembly of mechanical systems, the dual-arm robot is always used for cabin docking. In order to ensure the accuracy and reliability of cabin docking, a multi-objective trajectory planning method for the dual-arm robot was proposed. A kinematic model of the dual-arm robot was constructed based on the Denavit–Hartenberg (D-H) method firstly. Then, in the Cartesian space, the end trajectory of the dual-arm robot was confirmed by the fifth-order B-spline curve. On the basis of a traditional multi-objective cuckoo search algorithm, a modified cuckoo algorithm was built using the improved initial population generation method and the step size. The total consumption time and joint impact were selected as the objective functions, the overall optimal solution for the modified cuckoo algorithm was obtained using the normalized evaluation method. The optimal trajectory planning was achieved. Finally, the feasibility and effectiveness of the trajectory planning method were verified with the experiments.

Blockchain-Enabled Infection Sample Collection System Using Two-Echelon Drone-Assisted Mechanism

The collection and transportation of samples are crucial steps in stopping the initial spread of infectious diseases. This process demands high levels of safety and timeliness. The rapid advancement of technologies such as the Internet of Things (IoT) and blockchain offers a viable solution to this challenge. To this end, we propose a Blockchain-enabled Infection Sample Collection system (BISC) consisting of a two-echelon drone-assisted mechanism. The system utilizes collector drones to gather samples from user points and transport them to designated transit points, while deliverer drones convey the packaged samples from transit points to testing centers. We formulate the described problem as a Two-Echelon Heterogeneous Drone Routing Problem with Transit point Synchronization (2E-HDRP-TS). To obtain near-optimal solutions to 2E-HDRP-TS, we introduce a multi-objective Adaptive Large Neighborhood Search algorithm for Drone Routing (ALNS-RD). The algorithm’s multi-objective functions are designed to minimize the total collection time of infection samples and the exposure index. In addition to traditional search operators, ALNS-RD incorporates two new search operators based on flight distance and exposure index to enhance solution efficiency and safety. Through a comparison with benchmark algorithms such as NSGA-II and MOLNS, the effectiveness and efficiency of the proposed ALNS-RD algorithm are validated, demonstrating its superior performance across all five instances with diverse complexity levels.

Research on location-routing optimization of distribution center for emergency supplies based on IMOCS-LNS hybrid algorithm

This paper establishes a location-routing optimization model of the distribution center for emergency supplies with the goals of system reaction time, total cost of consumption, psychological fear of the populace in disaster-affected locations, and material usage rate. Where the excess time, demand, and penalty coefficient are the components of the penalty cost in the total consumption cost, and where the psychological panic of those in the affected area is represented by the psychological perception function of panic developed in accordance with the prospect theory. An improved hybrid multi-objective cuckoo-large-neighborhood search algorithm was then designed to introduce tent mapping, nonlinear inertia weights, elite strategies, congestion operators, and dynamically adjusted discovery probabilities into the standard multi-objective cuckoo optimization algorithm, which generates a new solution using a large-neighborhood search algorithm after discarding part of the solution with the discovery probability, and then accepts the current nondominated solution with dynamic probabilities. The paper uses the improved algorithm to solve Christofides69, an arithmetic example from the standard dataset of the LRP problem, and the results show that the solution provided by the improved algorithm outperforms the solutions provided by the standard multi-objective cuckoo search algorithm and the NSGA-II algorithm in terms of the total cost of dissipation, the level of psychological panic of the people in the affected area, the rate of utilization of the supplies, and the number of distribution centers open. Finally, the improved algorithm was used to analyze cases of different sizes separately, and it was found that the algorithm yielded better results and was therefore able to demonstrate its effectiveness.

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

MOBCSA: Multi-Objective Binary Cuckoo Search Algorithm for Features Selection in Bioinformatics

MOBCSA: Multi-Objective Binary Cuckoo Search Algorithm for Features Selection in Bioinformatics

Retracted: A FJSSP Method Based on Dynamic Multi-Objective Squirrel Search Algorithm

Retracted: A FJSSP Method Based on Dynamic Multi-Objective Squirrel Search Algorithm

Combined heat and power economic emission dispatch using dynamic switched crowding based multi-objective symbiotic organism search algorithm

Combined heat and power economic emission dispatch (CHPEED) problem is a highly complex, non-linear, non-convex multi-objective optimization problem due to two conflicting objectives and various operational constraints such as valve-point loading effect, power transmission loss, prohibited operating zone, and the feasible operating region of combined heat and power unit. In order to overcome these challenges, it is necessary to design an algorithm that exhibits a search behavior, which is suitable for the characteristics of objective and constraint space of the CHPEED problem. For these reasons, a dynamic switched crowding based multi-objective symbiotic organism search (DSC-MOSOS) algorithm was designed to meet the requirements and geometric space of the CHPEED problem. By applying the DSC method in the MOSOS algorithm, it was aimed to improve the exploration ability, to strengthen exploitation-exploration balance, and to prevent the catching into local solution traps. A comprehensive experimental study was carried out to prove the performance of the proposed algorithm on IEEE CEC 2020 multi-modal multi-objective problems (MMOPs) and CHPEED problem. In the experimental study conducted among eleven versions of MOSOS variations created with DSC-method and the base MOSOS algorithm on IEEE CEC 2020 MMOPs, according to Friedman scores based on the four performance metrics, the base MOSOS algorithm ranked the last. In other experimental study, the best DSC-MOSOS variant was applied to solve the CHPEED problem, where 5-, 7-, 10- and 14-unit test systems and eight case studies were considered. The important points of this study were that 10-unit and 14-unit test systems were presented to the literature, and the prohibited operating zone was considered in CHPEED problem for the first time. According to the results obtained from eight case studies obtained from the DSC-MOSOS and fourteen competitor algorithms, while the improvement in cost was between 0.2% and 16.55%, the reduction of the emission value was between 0.2 kg and 42.97 kg compared to the competitor algorithms. On the other hand, the stability of the DSC-MOSOS and the base MOSOS was evaluated using stability analysis. While the MOSOS algorithms was not able to perform a success in any case study, the DSC-MOSOS was achieved an average success rate with 91.16%. Thus, the performance of the DSC-MOSOS over the MOSOS was verified by the results of experimental studies and analysis.

A hypervolume-based cuckoo search algorithm with enhanced diversity and adaptive scaling factor

Existing multi-objective cuckoo search (MOCS) algorithms are based on either Pareto dominance or decomposition. However, when dealing with complex multi-objective problems (MOPs), the Pareto dominance-based algorithms face a decrease in selection pressure, and the decomposition-based algorithms easily gain poor distributions. The objective of this paper is to repurpose an indicator-based MOCS by combining improved diversity enhancement (IDE) and adaptive scaling factor (ASF) for MOPs. In the proposed algorithm, hypervolume is used as the indicator to guarantee better convergence and enough spread of the population. IDE chooses the large hypervolume to rebuild the parent population to compensate for the lack of population diversity. Additionally, ASF makes full use of individuals information to enhance the search ability of Lévy component in cuckoo search. Comprehensive experiments on 31 benchmark functions including two classical suites ZDT, WFG, and one challenged suite proposed in CEC2019, as well as 8 real-world problems were conducted to test the proposed algorithm. Compared with several state-of-the-art multi-objective evolutionary algorithms, the effectiveness and efficiency of our proposed method were demonstrated by the results.

A new decomposition-based multi-objective symbiotic organism search algorithm for solving truss optimization problems

This study presents a comprehensive study on the potential and efficacy of decomposition-based multi-objective symbiotic organism search (MOSOS/D) algorithm for truss structure optimization. The investigation is carried out on five benchmark truss structures, e.g., 37-bar, 60-bar, 72-bar, 120-bar, and 200-bar truss problems. The optimization performance of MOSOS/D is compared with other established algorithms, such as Multi-Objective Evolutionary Algorithm Based on Decomposition, Non-Dominated Sorting Genetic Algorithm-II, Multi-Objective Equilibrium Optimizer, Multi-Objective Marine Predator Algorithm, Decomposition-Based Multi-Objective Heat Transfer Search, Multi-Objective Passing Vehicle Search, Multi-Objective Evolutionary Algorithm Based on Decomposition Differential Evaluation, Multi-Objective Symbiotic Organisms Search, and Multi-Objective Multi-Verse Optimizer considering several metrics, including Generational Distance, Spacing, Spread, Inverted Generational Distance, Hypervolume and Runtime. The findings demonstrate MOSOS/D’s competitiveness in providing non-dominated solutions with less space between them. It also achieves a good balance between convergence and coverage and, thus, delivers diverse solutions with less computational complexity. However, the efficiency of the MOSOS/D algorithm varies depending on the complexity of the considered truss structure, with some algorithms occasionally outperforming it in one or two aspects for specific benchmarks. This study provides valuable insights to engineers and designers aiming to achieve optimal truss structure design.

A multi-objective cooperation search algorithm for cascade reservoirs operation optimization considering power generation and ecological flows

With growing attention focused on energy generation and environmental conservation, the operation of cascade reservoirs that considers power generation benefits and ecological flow requirements plays an increasingly important role in water resource and power systems. However, the traditional optimization methods may fail to solve complex cascade reservoirs operation models with strong spatial-temporal coupling physical constraints. To effectively address this problem, this study proposes an efficient multi-objective cooperation search algorithm (MOCSA) that utilizes the modified team communication, reflective learning operator and internal competition operators to enhance global exploration and local exploitation. MOCSA is tested on a group of multi-objective benchmark functions and real-world constrained engineering problems. The results demonstrate that MOCSA can provide better results for most benchmark test functions in comparison with the competitive algorithms. Besides, MOCSA exhibits excellent search ability to find feasible solutions of constrained engineering problems. The proposed method is then applied to resolve a real-world reservoir system under different operation scenarios. Simulations indicate that MOCSA provides diversified decision options for the operation of cascade reservoir system and find a more diverse set of non-dominated solutions within the feasible solution space. Overall, this research presents MOCSA as an effective multi-objective optimization tool for complex cascade reservoir operation problems.

ETNAS: An energy consumption task-driven neural architecture search

Neural Architecture Search (NAS) is crucial in the field of sustainable computing as it facilitates the development of highly efficient and effective neural networks. However, it cannot automate the deployment of neural networks to accommodate specific hardware resources and task requirements. This paper introduces ETNAS, which is a hardware-aware multi-objective optimal neural network architecture search algorithm based on the differentiable neural network architecture search method (DARTS). The algorithm searches for a lower-power neural network architecture with guaranteed inference accuracy by modifying the loss function of the differentiable neural network architecture search. We modify the dense network in DARTS to simultaneously search for networks with a lower memory footprint, enabling them to run on memory-constrained edge-end devices. We collected data on the power consumption and time consumption of numerous common operators on FPGA and Domain-Specific Architectures (DSA). The experimental results demonstrate that ETNAS achieves comparable accuracy performance and time efficiency while consuming less power compared to state-of-the-art algorithms, thereby validating its effectiveness in practical applications and contributing to the reduction of carbon emissions in intelligent cyber–physical systems (ICPS) edge computing inference.

Wire Electrical Discharge Machining of AISI304 and AISI316 Alloys: A Comparative Assessment of Machining Responses, Empirical Modeling and Multi-Objective Optimization

This research investigates the multi-response of both material removal rate (MRR) and surface roughness (Ra) for the wire electrical discharge machining (WEDM) of two stainless steel alloys: AISI 304 and AISI 316. Experimental results are utilized to compare the machining responses obtained for AISI 316 with those obtained for AISI 304, as previously reported in the literature. The experimental work is conducted through a full factorial experimental design of five running parameters with different levels: applied voltage, transverse feed, pulse-on/pulse-off times and current intensity. The machined workpieces are analyzed using an image processing technique in order to evaluate the size of cut slots to allow the calculation of the MRR. Followed by the characterization of the surface roughness along the side walls of the slots. Different mathematical regression techniques were developed to represent the multi-response of both materials using the MATLAB regression toolbox. It was found that WEDM process parameters have a fuzzy influence on the responses of both material models. This allowed for multi-objective optimization of the regression models using four different techniques: multi-objective genetic algorithm (MOGA), multi-objective pareto search algorithm (MOPSA), weighted value grey wolf optimizer (WVGWO) and osprey optimization algorithm (OOA). The optimization results reveal that the optimal WEDM parameters of each response are inconsistent to the others. Hence, the optimal results are considered a compromise between the best results of different responses. Noteworthily, the multi-objective pareto search algorithm outperformed the other candidates. Eventually, the optimal results of both materials share the high voltage, high transverse feed rate and low pulse-off time parameters; however, AISI 304 requires low pulse-on time and current intensity levels while AISI 316 optimal results entail higher pulse-on time and current levels.

A Multi-Objective Simulated Annealing Local Search Algorithm in Memetic CENSGA: Application to Vaccination Allocation for Influenza

Flu vaccine allocation is of great importance for safeguarding public health and mitigating the impact of influenza outbreaks. In this regard, decision-makers face multifaceted challenges, including limited vaccine supply, targeting vulnerable people, adapting to regional variations, ensuring fairness in distribution, and promoting public trust. The objective of this work is to address the vaccination allocation problem by introducing a novel optimization scheme with the simulated annealing (SA) algorithm. A dual-objective model is developed to both manage infection rates and minimize the unit cost of the vaccination campaign. The proposed approach is designed to promote convergence toward the best Pareto front in multi-objective optimization, wherein SA attempts to embed diversity and uniformity within a memetic version of the controlled elitism nondominated sorting genetic algorithm (CENSGA). To model the underlying vaccination allocation problem, the dynamics of the disease are described using the susceptible–exposed–infectious–recovered (SEIR) epidemiological model to better express hidden flu characteristics. This model specifically analyzes the effects of pulsive vaccination allocation in two phases aiming to minimize the number of infected individuals to an acceptable level in a finite amount of time, which can help in stabilizing the model against sudden flu endemics over the long run. The computational experiments show that the proposed algorithm effectively explores the extensive search space of the vaccination allocation problem. The results of the suggested framework indicate that the obtained Pareto front best represents complete vaccination campaigns. The findings of this research can help in evidence-based decision making that can optimize flu vaccine distribution, contribute to the prevention of illness and reduction in hospitalizations, and potentially save countless lives.