This is an addendum to the article ’Pal, A. and Charkhgard, H, “A feasibility pump based heuristic for multi-objective mixed integer linear programming”, Computers & Operations Research 112:104760, ...2019. In that work, a comparison was made to another method – a multi-directional local search. This was done using a pre-existing code. After the above work was published, evidence emerged that the mentioned pre-existing code contained a bug. In this addendum note, that bug is corrected and updated comparative results are provided.
•There was a then-unknown bug of third-party code.•A small portion of previously published result were affected by the bug.•We fix this bug and provide updated results.
Abstract
The construction of hybrid power plants with renewable resources can bring significant economic benefits if it is evaluated economically and technically. The present study uses a novel ...optimum methodology for designing a combined solar/battery/diesel system in Yarkant, Xinjiang Uyghur Autonomous Region of China. In the desired system, the green energy combined system is designed to reduce the use of diesel generators. The diesel generator has been used in the photovoltaic, diesel, and battery to support green energy resources and batteries, as well as function as a backup generator for critical times whenever the production of green energy resources is low or the load demand is high. The amount of CO2 emitted, the probability of load shortage and the system cost on yearly basis are the major goals in the process of optimization. Here, the single‐objective problem is created by using the ε‐constraint technique to combine the many objectives. An improved Henry gas solubility optimizer handles the problem of optimization. To demonstrate the superiority of the strategy, a comparison is conducted between the simulation outcomes of the offered system, HOMER, and particle swarm optimizer ‐based optimum systems from the literature. The sensitivity of each parameter is also examined using sensitivity analysis.
Dynamic multi-objective optimization problems (DMOPs) are multi-objective optimization problems in which at least one objective and/or related parameter vary over time. The challenge of solving DMOPs ...is to efficiently and accurately track the true Pareto-optimal set when the environment undergoes changes. However, many existing prediction-based methods overlook the distinct individual movement directions and the available information in the objective space, leading to biased predictions and misleading the subsequent search process. To address this issue, this paper proposes a prediction method called IMDMOEA, which relies on cluster center points and induced mutation. Specifically, employing linear prediction methods based on cluster center points in the decision space enables the algorithm to rapidly capture the population's evolutionary direction and distributional shape. Additionally, to enhance the algorithm's adaptability to significant environmental changes, the induced mutation strategy corrects the population's evolutionary direction by selecting promising individuals for mutation based on the predicted result of the Pareto front in the objective space. These two complementary strategies enable the algorithm to respond faster and more effectively to environmental changes. Finally, the proposed algorithm is evaluated using the JY, dMOP, FDA, and F test suites. The experimental results demonstrate that IMDMOEA competes favorably with other state-of-the-art algorithms.
This paper considers the fault detection problem of closed-loop Takagi-Sugeno (T-S) fuzzy systems with unknown system dynamics. However, the unknown dynamics make the model-based detection methods ...being infeasible. To tackle this problem, a detection scheme is designed directly by using input/state data, and disturbance attenuation as well as fault sensitivity performance are then introduced within data-driven framework such that a multiobjective optimization problem is formulated to compute the parameters of detector. Moreover, to remove the existing limitation that sensor faults must occur, corresponding design conditions of detector are developed by considering the characteristics of fault frequency. In particular, a linearization approach via searching the minimum singular value of unknown matrix is further developed to handle the nonconvex problem caused by introducing the fault sensitivity performance. Finally, an aero-engine system is used to show the effectiveness and advantages of the developed method.
•Proposing a binary differential evolution algorithm with self-learning strategy, called MOFS-BDE, to solve multi-objective feature selection problems.•Proposing a new binary mutation operator based ...on probability difference to guide the individuals to locate potentially optimal areas fast.•Proposing a new one-bit purifying search operator (OPS) for improving the self-learning capability of elite individuals.•Proposing an efficient non-dominated sorting operator with crowding distance to reduce the time consumption of the selection operator in differential evolution.
Feature selection is an important data preprocessing method. This paper studies a new multi-objective feature selection approach, called the Binary Differential Evolution with self-learning (MOFS-BDE). Three new operators are proposed and embedded into the MOFS-BDE to improve its performance. The novel binary mutation operator based on probability difference can guide individuals to rapidly locate potentially optimal areas, the developed One-bit Purifying Search operator (OPS) can improve the self-learning capability of the elite individuals located in the optimal areas, and the efficient non-dominated sorting operator with crowding distance can reduce the computational complexity of the selection operator in the differential evolution. Experimental results on a series of public datasets show that the effective combination of the binary mutation and OPS makes our MOFS-BDE achieve a trade-off between local exploitation and global exploration. The proposed method is competitive in comparison with some representative genetic algorithm-, particle swarm-, differential evolution-, and artificial bee colony-based feature selection algorithms.
Display omitted
This work proposes a novel yet practical dragonfly optimization algorithm that addresses four competing objectives simultaneously. The proposed algorithm is applied to a hybrid system driven by the ...solid oxide fuel cell (SOFC) integrated with waste heat recovery units. A function-fitting neural network is developed to combine the thermodynamic model of the system with the dragonfly algorithm to mitigate the calculation time. According to the optimization outcomes, the optimum parameters create significantly more power and have a greater exergy efficiency and reduced product costs and CO2 emissions compared to the design condition. The sensitivity analysis reveals that while the turbine inlet temperatures of power cycles are ineffective, the fuel utilization factor and the current density significantly impact performance indicators. The scatter distribution indicates that the fuel cell temperature and steam-to-carbon ratio should be kept at their lowest bound. The Sankey graph shows that the fuel cell and afterburner are the main sources of irreversibility. According to the chord diagram, the SOFC unit with a cost rate of 13.2 $/h accounts for more than 29% of the overall cost. Finally, under ideal conditions, the flue gas condensation process produces an additional 94.22 kW of power and 760,056 L/day of drinkable water.
•A novel optimization approach inspired by dragonflies' behavior is introduced.•The artificial neural network is applied to reduce the calculation time.•The feasibility of the method is studied by optimizing a SOFC-driven system.•A sensitivity analysis is done to assess the impact of optimization parameters.•A considerably better techno-economic-environmental condition is achieved.
Multiobjective optimization problems (MOPs) are the optimization problem with multiple conflicting objectives. Generally, an optimization algorithm can find a large number of optimal solutions for ...MOPs, which easily overwhelm decision makers (DMs) and make it difficult for decision-making. Preference-based evolutionary multiobjective optimization (EMO) aims to find the partial optima in the regions preferred by the DM. Although it narrows the scope of the optimal solutions, it usually still returns a population of optimal solutions (typically 100 or larger in EMO) with a small distance between adjacent optima. Top-K, which is a well-established research subject in many fields to find the best K solutions, may be a direction to reduce the number of optimal solutions. In this paper, first, we introduce the top-K notion into preference-based EMO and propose the top-K model to obtain the best K individuals of multiobjective optimization problems (MOPs). Then, with the top-K model, we propose NSGA-II-TopK and SPEA2-TopK to search for the top-K preferred solutions for preference-based continuous and combinatorial MOPs, respectively. Finally, the proposed algorithms with several representative preference-based EMO algorithms are compared in different preference situations for MOPs. Experimental results show the proposed algorithms have strong performances against the compared algorithms.
Summary
Privacy data security has become an important bottleneck for the overall development of artificial intelligence and a key challenge that needs to be broken in the Internet era. The current ...research mainly considers differential privacy to effectively protect the private information in the data. However, as the noise increases, the precision of the training model will decrease. In order to solve above problem, an adaptive differential privacy (ADP) method is constructed and applied to deep neural networks. ADP adds noise adaptively in the training process according to the importance of features. We also build the differential privacy multi‐objective optimization model (DPMOM). DPMOM adopts multi‐objective optimization characteristics, takes accuracy and privacy protection as the optimization objectives. It optimizes the super parameters of deep neural networks and the noise of differential privacy. In addition, to better solve the ADP model, with the NSGA‐II algorithm as the basic framework, a multi‐objective optimization algorithm based on differential privacy protection (DPPMOA) is designed. Simulation experiments show that compared with other machine learning methods and differentially private stochastic gradient descent, the accuracy of ADP is higher under the same amount of noise. Through comparison with NSGA‐II, IBEA, PESA‐II, and AGE‐II, DPPMOA is proved that the solution set of this algorithm is better.
