Thermal and Economical Optimization of a Shell and Tube Evaporator Using Hybrid Backtracking Search-Sine-Cosine Algorithm

WOS: 000399158300035This paper proposes a hybrid optimization algorithm based on the combination of the merits of the backtracking search (BSA) and sine-cosine algorithm (SCA) to achieve the optimal design of a shell and tube evaporator. To the author's best knowledge, this is the first application of the metaheuristic algorithms over shell and tube evaporator design problems. In order to test the accuracy of the proposed hybrid algorithm, 10 well-known optimization test functions have been solved. Numerical results obtained from the hybrid BSA-SCA have been compared with the literature optimizers including differential search, big bang-big crunch optimization, quantum-behaved particle swarm optimization, bat algorithm, intelligent tuned harmony search algorithm, and backtracking search algorithm. Comparison results reveal that solutions obtained from the BSA-SCA are better than those of the results acquired by the aforementioned optimizers with respect to statistical analysis. Proposed optimization procedure is then utilized to obtain optimum values of the two heat exchanger design objectives including total cost and overall heat transfer coefficient. Six decision variables such as tube outer diameter, shell diameter, baffle spacing, tube length, number of tube passes, and tube bundle configuration are selected to be iteratively optimized. It is found that BSA-SCA provides better results than the compared literature optimizers for both objective functions. In addition, a sensitivity analysis is performed for the design parameters at the optimal point. Results show that variation of the design parameters at the optimum point has considerable effect on the objective function rates

Multi objective design optimization of plate fin heat sinks using improved differential search algorithm

This study provides the multi-objective optimization of plate fin heat sinks equipped with flow – through and impingement-flow air-cooling system by using Improved Differential Search algorithm. Differential Search algorithm mimics the subsistence characteristics of the living beings through the migration process. Convergence speed of the algorithm is enhanced with the local search based perturbation schemes and this improvement yields favorable solution outputs according to the results obtained from the widely quoted optimization test problems. Improved algorithm is employed on multi-objective design optimization of plate fins heat sink considering the objective functions of entropy generation rate and total material cost. Total of seven decision variables such as oncoming stream velocity, number of fins on the plate, gap between consecutive fins, base thickness of the plate, width, length and height of the plate fin heat sink are selected to be optimized. Pareto frontiers are constructed for both flow-through and impingement flow air-cooling system design and best solutions are obtained by means of widely reputed decision-making theories of LINMAP, TOPSIS, and Shannon’s entropy theory. Results retrieved from the case studies show that reliable outcomes could be achieved in terms of solution accuracy through Improved Differential Search optimizerThis study provides the multi-objective optimization of plate fin heat sinks equipped with flow – through and impingement-flow air-cooling system by using Improved Differential Search algorithm. Differential Search algorithm mimics the subsistence characteristics of the living beings through the migration process. Convergence speed of the algorithm is enhanced with the local search based perturbation schemes and this improvement yields favorable solution outputs according to the results obtained from the widely quoted optimization test problems. Improved algorithm is employed on multi-objective design optimization of plate fins heat sink considering the objective functions of entropy generation rate and total material cost. Total of seven decision variables such as oncoming stream velocity, number of fins on the plate, gap between consecutive fins, base thickness of the plate, width, length and height of the plate fin heat sink are selected to be optimized. Pareto frontiers are constructed for both flow-through and impingement flow air-cooling system design and best solutions are obtained by means of widely reputed decision-making theories of LINMAP, TOPSIS, and Shannon’s entropy theory. Results retrieved from the case studies show that reliable outcomes could be achieved in terms of solution accuracy through Improved Differential Search optimize

Hybrid Chaotic Quantum behaved Particle Swarm Optimization algorithm for thermal design of plate fin heat exchangers

WOS: 000367020900005This study investigates the utilization of Hybrid Chaotic Quantum behaved Particle Swarm Optimization (HCQFSO) algorithm for thermal design of plate fin heat exchangers. HCPQSO algorithm successfully combines a variant of Quantum behaved Particle Swarm Optimization (LQPSO), with efficient local search mechanisms to yield better results in terms of solution accuracy and convergence rate. Hot and cold side length of the heat exchanger, fin height, fin frequency (fins per meter), fin thickness, lance length of the fin and number of fin layers are considered as design variables to minimize the heat transfer area, total pressure drop and total cost of heat exchanger with a specified heat duty under a given search space. Constraint handling is maintained with the Automatic Dynamic Penalization method which is adaptive and does not need of tuning the penalty coefficient for any optimization problem. The robustness of the proposed algorithm is benchmarked with various types of optimization test problems and case studies taken from the literature. Comparison results indicate that hybrid algorithm outperforms many optimization algorithms available in the literature. It is also observed that the proposed algorithm successfully converges to optimum configuration with a higher accuracy. (C) 2015 Elsevier Inc. All rights reserved

Comparative investigation and multi objective design optimization of R744/R717, R744/R134a and R744/R1234yf cascade rerfigeration systems

WOS: 000457651400017This study aims to make a comparative investigation on performance analysis of cascade refrigeration systems using R744/R717, R744/R134a, and R744/R1234yf refrigerant pairs. Artificial Cooperative Search methaheuristic algorithm is put into practice to obtain the optimal values of eight design parameters including Condenser and evaporator temperature, R744 condensing temperature, temperature difference in the cascade condenser, and amount of subcooling and superheating at the bottom and the top of the cascade cycle. Second law efficiency and total annual cost of the cascade refrigeration system are chosen as design objectives to be optimized individually and concurrently in order to obtain the optimal operating conditions of the system. Single optimization results show that R744/R1234yf system has the lowest operating cost while having the highest second law efficiency compared to other cycle configurations. A set of non-dominated solutions obtained through multi objective Artificial Cooperative Search algorithm is represented in the form of Pareto front and the best result is chosen from the well-reputed decision makers of TOPSIS and LINMAP for each cycle configuration. Multi objective optimization results reveal that design variables of the refrigeration system can create a trade off between problem objectives. A sensitivity analysis is performed to investigate the influences of varying values of design variables upon problem objectives while the system is operated under optimal conditions

Global best-guided oppositional algorithm for solving multidimensional optimization problems

Turgut, Oguz Emrah/0000-0003-3556-8889WOS: 000520227700004This paper presents an alternative optimization algorithm to the literature optimizers by introducing global best-guided oppositional-based learning method. the procedure at hand uses the active and recent manipulation schemes of oppositional learning procedure by applying some modifications to them. the first part of the algorithm deals with searching the optimum solution around the current best solution by means of the ensemble learning-based strategy through which unfeasible and semi-optimum solutions have been straightforwardly eliminated. the second part of the algorithm benefits the useful merits of the quasi-oppositional learning strategy to not only improve the solution diversity but also enhance the convergence speed of the whole algorithm. A set of 22 optimization benchmark functions have been solved and corresponding results have been compared with the outcomes of the well-known literature optimization algorithms. Then, a bunch of parameter estimation problem consisting of hard-to-solve real world applications has been analyzed by the proposed method. Following that, eight widely applied constrained benchmark problems along with well-designed 12 constrained test cases proposed in CEC 2006 session have been solved and evaluated in terms of statistical analysis. Finally, a heat exchanger design problem taken from literature study has been solved through the proposed algorithm and respective solutions have been benchmarked against the prevalent optimization algorithms. Comparison results show that optimization procedure dealt with in this study is capable of achieving the utmost performance in solving multidimensional optimization algorithms

Local search enhanced Aquila optimization algorithm ameliorated with an ensemble of Wavelet mutation strategies for complex optimization problems

Aquila Optimization Algorithm (AQUILA) is a newly emerged metaheuristic optimizer for solving global optimization problems, which is based on intrinsic hunting behaviors of the foraging aquila individuals. However, this stochastic optimization method suffers from some algorithm-specific drawbacks, such as premature convergence to the local optimum points over the search hyperspace due to the lack of solution diversity in the population. To conquer this algorithmic deficiency, an ensemble of Wavelet mutation operators has been implemented into the standard AQUILA to enhance the explorative capabilities of the algorithm by diversifying the search domain as much as possible. Furthermore, a brand-new local search scheme empowered by the synergetic interactions of elite opposition-based learning and a simple-yet-effective exploitative manipulation equation is introduced into the base AQUILA to intensify on the previously visited promising regions. The proposed learning schemes are stochastically applied to the obtained solutions from the base Aquila algorithm to refine the overall solution quality and amend the premature convergence problem. It is also aimed to investigate whether the collective application of Wavelet mutation operators with different types entails a significant improvement in the general search effectivity of the algorithm rather than their individual efforts. Numerical experiments made on a suite of unconstrained unimodal and multimodal benchmark functions reveal that this hybridization with AQUILA has improved the general solution accuracy and stability to very high standards, outperforming its contemporary counterparts in the comparative statistical analysis. Furthermore, an exhaustive benchmark analysis has been performed on fourteen constrained real-world complex engineering problems.(c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved

Ensemble Shuffled Population Algorithm for multi-objective thermal design optimization of a plate frame heat exchanger operated with Al2O3/water nanofluid

WOS: 000438775200014This study proposes a brand new optimization algorithm entitled Ensemble Shuffled Population Algorithm for solving multidimensional optimization problems. The proposed algorithm adopts the perturbation equations of the Crow Search and Differential Search algorithms with useful modifications on them and aims to maintain a reasonable balance between the intensification and diversification phases of the algorithm. A batch of 22 benchmark problems consisting of unimodal and multimodal unconstrained optimization test functions are applied using this algorithm to assess its performance on multi dimensional problems. Statistical results obtained from the proposed Ensemble Shuffled Population Algorithm are compared to those found by eleven well known metaheuristic optimizers. The comparison results show that the Ensemble Shuffled Population Algorithm outperforms the compared optimizers with regards to solution accuracy and convergence speed. After that, the proposed algorithm is applied on a multi objective optimization of a plate frame heat exchanger operated with Al2O3 nanofluid. The optimization results show that utilizing nanoparticles instead of base fluid not only increases the overall heat transfer coefficient rates but also entails a huge decline in total cost values. A Pareto frontier is constructed for these two conflicting objectives to select the final optimum solution from the set of non-dominated solutions by virtue of three famous decision making methods of LINMAP, TOPSIS, and Shannon's entropy theory. Then, sensitivity analysis is performed to observe the variational effects of the design variables on the optimization objectives. (C) 2018 Elsevier B.V. All rights reserved

Differential evolution based global best algorithm: an efficient optimizer for solving constrained and unconstrained optimization problems

Turgut, Oguz Emrah/0000-0003-3556-8889; Turgut, Mert Sinan/0000-0002-5739-2119WOS: 000532826500088This study proposes an optimization method called Global Best Algorithm for successful solution of constrained and unconstrained optimization problems. This propounded method uses the manipulation equations of Differential Evolution, dexterously combines them with some of the perturbation schemes of Differential Search algorithm, and takes advantages of the global best solution obtained on the course of the iterations to benefit the productive and feasible in the search span through which the optimum solution can be easily achieved. A set of 16 optimization benchmark functions is then applied on the proposed algorithm as well as some of the cutting edge optimizers. Comparative study between these methods reveals that GBEST has the ability to achieve more competitive results when compared to other algorithms. Effects of algorithm parameters on optimization accuracy have been benchmarked with some high-dimensional unimodal and multimodal optimization test functions. Five real world design problems accompanied with three challenging test functions have been solved and verified against the literature approaches. Optimal solution obtained for economic dispatch problem also proves the applicability of the proposed method on multidimensional constrained problems with having large solution spaces