This paper proposes an evolutionary algorithm for regional communities based on cooperation mechanisms, taking into account the objective evolutionary goals of different countries and regions in historical processes, ...
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This study aimed to minimize the tumor cell population using minimal medicine for chemotherapy treatment, while maintaining the effector-immune cell population at a healthy threshold. Therefore, a mathematical model w...
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This study aimed to minimize the tumor cell population using minimal medicine for chemotherapy treatment, while maintaining the effector-immune cell population at a healthy threshold. Therefore, a mathematical model was developed in the form of ordinary differential equations (ODE), and the solution to the Multi-Objective Optimal Control Problem (MOOCP) was obtained using Multi-Objective Optimization algorithms. In this study, the interaction of the tumor cell and effector cell populations with chemotherapy was investigated using Pure MOOCP and Hybrid MOOCP methods. The handling of constraints and the Pontryagin Maximum Principle (PMP) differ among these methods. Swarm Intelligence (SI) and evolutionary algorithms (EA) were used to process the results of these methods. The numerical outcomes of SI and EA are displayed via the Pareto Optimal Front. In addition, the solutions from these algorithms were further analyzed using the Hypervolume Indicator. The findings of this study demonstrate that the Hybrid Method outperforms Pure MOOCP via Multi-Objective Differential Evolution (MODE). MODE produces a point on the Pareto Optimal Front with a minimal distance to the origin, where the distance represents the best solution.
In the present paper, we demonstrate the possibilities of designing quantum computing circuits using a specific swarm intelligence algorithm - iSOMA in the form of three experiments. All simulations are based on a sim...
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In the present paper, we demonstrate the possibilities of designing quantum computing circuits using a specific swarm intelligence algorithm - iSOMA in the form of three experiments. All simulations are based on a simple sample of a quantum computing circuit from the Qiskit environment, which was used as a comparison circuit with the results of the three experiments already mentioned. In the first experiment, we try to find an arbitrary functional solution using iSOMA with minimal constraints on this circuit's design. It can be said that in this experiment, iSOMA showed the highest degree of "creativity". In the second experiment, we focused on whether iSOMA can be used to find a circuit identical to the one designed by a human or equivalent with the positions of the measurement gates fixed. In the last experiment, we highlight iSOMA's ability to avoid unnecessary qubit usage by adding redundant qubits to a possible circuit and fixing the measurement gates to the last two qubits in the scheme. In all three experiments, we see that iSOMA can find efficient functional and often astonishing solutions - the proposed method applied to a classical circuit founded a new one preserving required properties while saving one ancilla (redundant, useless, non-used)1 qubit. All computations are implemented in the IBM Qiskit2 environment. Although these are relatively simple experiments, the results show that evolutionary algorithms can successfully design more complex quantum circuits.
The rapid industrial development in the human society has brought about the air pollution, which seriously affects human health. PM2.5 concentration is one of the main factors causing the air pollution. To accurately ...
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The rapid industrial development in the human society has brought about the air pollution, which seriously affects human health. PM2.5 concentration is one of the main factors causing the air pollution. To accurately predict PM2.5 microns, we propose a dendritic neuron model (DNM) trained by an improved state-of-matter heuristic algorithm (DSMS) based on STL-LOESS, namely DS-DNM. Firstly, DS-DNM adopts STL-LOESS for the data preprocessing to obtain three characteristic quantities from original data: seasonal, trend, and residual components. Then, DNM trained by DSMS predicts the residual values. Finally, three sets of feature quantities are summed to obtain the predicted values. In the performance test experiments, five real-world PM2.5 concentration data are used to test DS-DNM. On the other hand, four training algorithms and seven prediction models were selected for comparison to verify the rationality of the training algorithms and the accuracy of the prediction models, respectively. The experimental results show that DS-DNM has the more competitive performance in PM2.5 concentration prediction problem.
A new evolutionary algorithm with optimal recombination is proposed for the total weighted tardiness problem on the single machine. We solve the optimal recombination problem in a crossover operator. The NP-hardness o...
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Optimization makes processes, systems, or products more efficient, reliable, and with better outcomes. A popular topic on optimization today is multiobjective bilevel optimization (MOBO). In MOBO, an upper level probl...
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Optimization makes processes, systems, or products more efficient, reliable, and with better outcomes. A popular topic on optimization today is multiobjective bilevel optimization (MOBO). In MOBO, an upper level problem is constrained by the solution of a lower level one. The problem at each level can include multiple conflicting objective functions and its own constraints. This survey aims to study the solution approaches proposed to solve MOBO problems, including exact methods and approximate techniques such as metaheuristics (MHs). This work explores classical literature to investigate why most classical methods, theories, and algorithms focus on linear and some convex MOBO problems to solve the optimistic MOBO. Moreover, we study and propose a taxonomy of MH-based frameworks for solving some MOBO instances, highlighting the pros and cons of five main approaches. Finally, a growing interest in MOBO has been detected in the optimization community. A significant number of possible applications and solution approaches establish an early research line to find solutions to these types of problems.
This work presents a concurrent design and multi-objective optimisation framework of horizontal axis wind turbine blades, made of composite material, for low wind speed. The optimisation model aims to minimise the str...
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This work presents a concurrent design and multi-objective optimisation framework of horizontal axis wind turbine blades, made of composite material, for low wind speed. The optimisation model aims to minimise the structural mass of the blade whilst simultaneously maximising the turbine power output, subjected to three constraints viz. blade tip deflection, and Tsai-Hill and von Mises criteria. The design variables are blade shape and details of the internal blade structure. The control points and polynomial interpolation technique were adopted to determine the blade shape while the airfoil types at blade sections remained fixed. The internal blade structure design variables include the thickness of ribs and spars and the carbon fibre thickness and orientations. The blade element momentum approach is utilised to calculate turbine power and structural loads, whereas a finite element method is employed for structural analysis. Twelve multi-objective metaheuristics algorithms are used to solve the proposed multi-objective optimisation problem while their performance is investigated. The results obtained show that the multi-objective cuckoo search algorithm is the most efficient method. This study is said to be the baseline for a future study on multi-objective optimisation which combines two design stages of the composite low-speed wind turbine blades.
In this study, industrial styrene reactors were optimized using the multi-objective algorithm Generalized Differential Evolution 3 (GDE3) to maximize their conversion and selectivity. When modeling the reac-tors, an i...
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In this study, industrial styrene reactors were optimized using the multi-objective algorithm Generalized Differential Evolution 3 (GDE3) to maximize their conversion and selectivity. When modeling the reac-tors, an intrinsic heterogeneous reaction model was adopted to produce realistic results, which would adequately encompass the complex influence of the decision variables in the system. Several optimal sce-narios were obtained using two-and three-objective approaches, which can be used in integrated process analysis to define suitable operational conditions. These scenarios were studied from a fundamental per-spective to explore the impact of the steam-to-ethylbenzene molar feed ratio, the number of catalyst beds, catalyst loading, operating pressure, and inlet temperatures on reactor performance. Furthermore, our GDE3 implementation has been made available in a public code repository and a Python package.(c) 2022 Elsevier Ltd. All rights reserved.
Efficient and stable global optimizers constitute a noteworthy arena of academic study and real-world applications. Since Multi-scale Quantum Harmonic Oscillator Algorithm inspired by the quantum motion for solving op...
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Efficient and stable global optimizers constitute a noteworthy arena of academic study and real-world applications. Since Multi-scale Quantum Harmonic Oscillator Algorithm inspired by the quantum motion for solving optimization problems was proposed, considerable contributions regarding this algorithm have been achieved in recent years. Nevertheless, issues such as the aggregation effect during sampling as well as recurrence and blindness in random searches hinder the performance of the algorithm. Motivated by this situation, a variant of Multi-scale Quantum Harmonic Oscillator Algorithm is put forward to improve the efficiency of the system convergence while maintaining the solution diversity. The measurement of the solution position through the collapse of the quantum state to the classical state is realized by means of quantum Monte Carlo simulations, and the energy position is established as a metric for energy observation. Then, the adaptive correction of the energy position is explored to improve algorithm performance. The core idea of our mechanism is to adaptively guide the candidate solutions toward convergence to the ground state by means of attractive factors based on the relationship among the energy positions of several reference points. Experimental results obtained on the CEC2013 benchmark functions and a real-world application indicate that the performance of our scheme is competitive and that it achieves prominence among the compared algorithms as the dimensionality increases.& COPY;2023 Elsevier B.V. All rights reserved.
A robust image classifier can model a classification function well and has subliminal impacts on classification performance when the input data is corrupted in some ways. Many popular classifiers based on deep neural ...
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