The use of the optimum series of heat exchangers rather than ones individually produced may benefit both manufacturers and users. A method for the optimization of a series of heat exchangers has been presented. A poss...
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The use of the optimum series of heat exchangers rather than ones individually produced may benefit both manufacturers and users. A method for the optimization of a series of heat exchangers has been presented. A possible decrease of around 20% in the capital cost of apparatus produced in a series has been estimated in comparison with individually produced units. The method can be extended to other series of apparatus used by the chemical process industries.
One of the most recent methods of structural damage identification is using the difference between structures responses after and before damage occurrence. To do this one can formulate the damage detection problem as ...
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One of the most recent methods of structural damage identification is using the difference between structures responses after and before damage occurrence. To do this one can formulate the damage detection problem as an inverse optimization problem where the extents of damage in each element are considered as the optimizations variables. To optimize the objective function, heuristic methods such as GA, PSO etc. are widely utilized. In this paper, inspired by animals such as bat, dolphin, oilbird, shrew etc. that use echolocation for finding food, a new and efficient method, called Echolocation Search algorithm (ESA), is proposed to properly identify the site and extent of multiple damage cases in structural systems. Numerical results show that the proposed method can reliably determine the location and severity of multiple damage cases in structural systems.
Purpose Highly non-linear optimization problems exist in many practical engineering applications. To deal with these problems, this study aims to propose an improved optimization algorithm, named, adaptive resistance ...
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Purpose Highly non-linear optimization problems exist in many practical engineering applications. To deal with these problems, this study aims to propose an improved optimization algorithm, named, adaptive resistance and stamina strategy-based dragonfly algorithm (ARSSDA). Design/methodology/approach To speed up the convergence, ARSSDA applies an adaptive resistance and stamina strategy (ARSS) to conventional dragonfly algorithm so that the search step can be adjusted appropriately in each iteration. In ARSS, it includes the air resistance and physical stamina of dragonfly during a flight. These parameters can be updated in real time as the flight status of the dragonflies. Findings The performance of ARSSDA is verified by 30 benchmark functions of Congress on Evolutionary Computation 2014's special session and 3 well-known constrained engineering problems. Results reveal that ARSSDA is a competitive algorithm for solving the optimization problems. Further, ARSSDA is used to search the optimal parameters for a bucket wheel reclaimer (BWR). The aim of the numerical experiment is to achieve the global optimal structure of the BWR by minimizing the energy consumption. Results indicate that ARSSDA generates an optimal structure of BWR and decreases the energy consumption by 22.428% compared with the initial design. Originality/value A novel search strategy is proposed to enhance the global exploratory capability and convergence speed. This paper provides an effective optimization algorithm for solving constrained optimization problems.
Multi-modal optimization is a troublesome problem faced by optimization algorithms. The multiscale quantum harmonic oscillator algorithm (MQHOA) utilizes group statistics strategy to evaluate the state of the populati...
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Multi-modal optimization is a troublesome problem faced by optimization algorithms. The multiscale quantum harmonic oscillator algorithm (MQHOA) utilizes group statistics strategy to evaluate the state of the population and neglects the individual state. It will lead the particles to be trapped in local optima when addressing multi-modal optimization problems. This paper proposes a modified MQHOA by introducing strict metastability constraints strategy (MQHOA-SMC). The new strategy adopts a joint constraint mechanism to make the particle states mutual constraint with each other. The modified algorithm enhances the ability to find a better quality solution in local areas. To demonstrate the efficiency and effectiveness of the proposed algorithm, simulations are carried out with SPSO2011, ABC, and QPSO on classical benchmark functions and with the newly CEC2013 test suite, respectively. The computational results demonstrate that MQHOA-SMC is a competitive algorithm for multi-modal problems.
We have used the teaching-learning-based optimization (TLBO) algorithm to design the plasmonic nano rods in order to achieve the maximum absorption coefficient spectrum. In TLBO, a group of learners controls the nano-...
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We have used the teaching-learning-based optimization (TLBO) algorithm to design the plasmonic nano rods in order to achieve the maximum absorption coefficient spectrum. In TLBO, a group of learners controls the nano-rod radius, the nano-rods distance, and the number of nano-rods in two dimensions. This approach is useful in optical applications such as solar cell and plasmonic nano antenna. (C) 2015 Elsevier GmbH. All rights reserved.
Social Learning Particle Swarm optimization (SL-PSO) greatly improves the optimization performance of PSO. In solving complex optimization problems, however, it still has some deficiencies, such as poor search ability...
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Social Learning Particle Swarm optimization (SL-PSO) greatly improves the optimization performance of PSO. In solving complex optimization problems, however, it still has some deficiencies, such as poor search ability and low search efficiency. Hence, an improved SL-PSO, namely, Three-Learning Strategy PSO (TLS-PSO) is proposed in this paper. Firstly, a med-point-example learning strategy and a random learning strategy are proposed to replace the imitation component and social influence component of SL-PSO to enhance the exploitation and exploration, respectively. Secondly, the two learning strategies are combined cleverly into an updating equation to balance exploration and exploitation. Finally, a worst-best example learning strategy is merged skillfully to construct TLS-PSO with hybrid learning mechanism and further enhance the search ability. The experimental results on the complex functions from CEC2013 and CEC2017 test sets indicate that TLS-PSO has better performance compared with state-of-the-art PSO variants and other algorithms. For example, TLS-PSO has an advantage over SL-PSO on 50 of the 56 functions from CEC2013, its running time is less than SL-PSO's and it has higher search efficiency. Simulation results on the 10 engineering problems also show that TLS-PSO outperforms 7 excellent algorithms, such as IUDE and iLSHADE,. It is expected to solve practical problems better. (C) 2022 Elsevier Inc. All rights reserved.
The development of vibration suppression systems with desired efficiency and low cost is one of the significant challenges in engineering. In this study, a parametric lattice model is considered to analyze the wave mi...
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The development of vibration suppression systems with desired efficiency and low cost is one of the significant challenges in engineering. In this study, a parametric lattice model is considered to analyze the wave mitigation features in the metamaterial based on a tetra-chiral topology of the periodic cell equipped with internal resonators. Bloch wave theorem and finite element method are employed to explore the bandgap of the structure and its wave mitigation features. Since the unit cell geometry can be designed to open and shift bandgaps, particle swarm optimization algorithm is used to find the largest possible gap in the desired frequency range. The optimization method is programmed using MATLAB combined with an in-house finite element solver, considering the parameters' ranges to ensure geometric compatibility. In all studied cases, the optimized geometry leads to superior vibration suppression and larger complete bandgaps.
This letter proposes a novel method to improve the results of the three-stage inversion algorithm, using polarimetric synthetic aperture radar interferometry. Since the accuracy of the estimated forest height is affec...
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This letter proposes a novel method to improve the results of the three-stage inversion algorithm, using polarimetric synthetic aperture radar interferometry. Since the accuracy of the estimated forest height is affected by the volume only coherence selection, finding the optimum coherence value is an important challenge for the conventional three-stage method. In the three-stage algorithm, a specific polarization state, HV, is usually used as the volume only channel. However, in this letter, an optimization algorithm is developed to find a more accurate volume only coherence on the coherence line. We used the experimental airborne SAR L-band single-baseline single-frequency polarimetric interferometry data to evaluate the proposed algorithm. The experimental results show the proposed optimized volume only coherence leads to 2.9-m improvement in the results of the three-stage inversion algorithm.
In literature, a lot of research works have been presented on crashworthiness in order to develop crash performance of vehicles and thin-wall structures. In this research, a new hybrid optimization algorithm based on ...
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In literature, a lot of research works have been presented on crashworthiness in order to develop crash performance of vehicles and thin-wall structures. In this research, a new hybrid optimization algorithm based on gravitational search algorithm and Nelder-Mead algorithm is introduced to improve crash performance of vehicles during frontal impact. The results show that the hybrid approach is very effective to develop crash performance of the vehicle components and thin-wall structures.
A novel quantum-inspired evolutionary algorithm is proposed based on the Bloch coordinates of quantum bits (qubits) in this paper. The chromosome is comprised of qubits whose Bloch coordinates comprise gene chain. The...
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A novel quantum-inspired evolutionary algorithm is proposed based on the Bloch coordinates of quantum bits (qubits) in this paper. The chromosome is comprised of qubits whose Bloch coordinates comprise gene chain. The quantum chromosomes are updated by quantum rotation gates, and are mutated by quantum non-gates. For the rotation direction of quantum rotation gates, a simple determining method is proposed. For the rotation and mutation of qubits, two new operators are constructed based on Bloch coordinates of qubits. In this algorithm, the Bloch coordinates of each qubit are regarded as three paratactic genes, each chromosome contains three gene chains, and each gene chain represents an optimization solution, which can accelerate the convergence process for the same number of chromosomes. By two application examples of function extremum and neural network weights optimization, the simulation results show that the approach is superior to common quantum evolutionary algorithm and simple genetic algorithm in both search capability and optimization efficiency. (c) 2007 Elsevier B.V. All rights reserved.
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