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This research study proposes a Q-learning-based metaheuristic algorithm framework for thermal design optimization of a shell-and-tube evaporator operating with different refrigerant mixtures, which is a highly complex real-world design problem and has not been investigated yet, in previous literature approaches before. The proposed method, called QL-HEUR, uses Q-learning as a high-level heuristic to iteratively guide the competitive recently emerged low-level metaheuristic algorithms. QL-HEUR is applied to 32 unconstrained optimization benchmark functions, and results are evaluated in statistical analysis. Moreover, three multidimensional constrained optimization problems will be solved. Respective solutions unravel that QL-HEUR is very effective in finding optimum solutions to constrained and unconstrained optimization problems. QL-HEUR is employed on the design optimization of a shell-and-tube heat exchanger running with different mixture pairs as a challenging real-world benchmark case. For the design case in which R134a–R1234yf (0.8:02) mixture is considered, 8.71% of the total cost is saved compared to the preliminary design of a heat exchanger operated with pure R1234yf refrigerant. For the second design case, the application of QL-HEUR results in a decrease of 8.93% for refrigerant composition R32–R134a (0.6:0.4) in comparison with the configuration running with pure R134a. It is also seen that the heat exchanger configuration running with pure R32 refrigerant yields the lowest total cost compared to the cases accomplished by varying mixture ratios of R290 and R32. It can be concluded that the optimum configuration of the heat exchanger operated with a refrigerant mixture can be conveniently employed for minimum total cost and global warming potential. [ABSTRACT FROM AUTHOR]

The scientific field of optimization has witnessed an increasing trend in the development of metaheuristic algorithms within the current decade. The vast majority of the proposed algorithms have been proclaimed as superior and highly efficient compared to their contemporary counterparts by their own developers, which should be verified on a set of benchmark cases if it is to give conducive insights into their true capabilities. This study completes a comprehensive investigation of the general optimization capabilities of the recently developed nature-inspired metaheuristic algorithms, which have not been thoroughly discussed in past literature studies due to their new emergence. To overcome this deficiency in the existing literature, optimization benchmark problems with different functional characteristics will be solved by some of the widely used recent optimizers. Unconstrained standard test functions comprised of thirty-four unimodal scalable optimization problems with varying dimensionalities have been solved by these competitive algorithms, and respective estimated solutions have been evaluated relying on the performance metrics defined by the statistical analysis of the predictive results. Convergence curves of the algorithms have been construed to observe the evolution trends of objective function values. To further delve into comprehensive analysis on unconstrained test cases, CEC 2013 problems have been considered for comparison tools since their resemblances of the following features of real-world complex algorithms. The optimization capabilities of eleven metaheuristics algorithms have been comparatively analyzed on twenty-eight multidimensional problems. Finally, fourteen complex engineering problems have been optimized by the algorithms to scrutinize their effectiveness on handling the imposed design constraints. [ABSTRACT FROM AUTHOR]

This theoretical research study proposes a novel Chaotic Quasi-Oppositional Arithmetic Optimization Algorithm (COAOA) for thermo-economic optimization of a shell and tube condenser working with refrigerant mixtures. Arithmetic Optimization Algorithm (AOA) is a recently emerged metaheuristic algorithm considering different mathematical operators to optimize the candidate solutions over a wide range of search domains. The effectiveness the COAOA is assessed by applying it to a set of benchmark optimization problems and comparing the obtained solutions with that of the original AOA and its quasi-oppositional variant. The COAOA has been employed to acquire the minimum value of the total annual cost of the shell and tube condenser by iteratively varying nine decision variables of mass flow rate, shell diameter, the tube inside diameter, tube length, number of tube passes, tube layout, tube pitch ratio, the total number of baffles, and diameter ratio. Three different case studies are solved using different refrigerant pairs used for in-tube flow to show the proposed metaheuristic optimizer's efficiency and effectivity on real-world mixed-integer optimization problem. Optimal results retrieved for different mixture pairs with varying mass fractions are compared with each other, and parametric configuration yielding the minimum total cost is decided. Finally, a comprehensive sensitivity analysis is performed to investigate the influences of the design variables over the considered problem objective. Overall analysis results indicate that COAOA can be an excellent optimizer to obtain a shell and tube condenser's optimal configuration within a reasonable computation time. [ABSTRACT FROM AUTHOR]

In this study, eight parameters are selected and their historical data are collected to predict the future of the energy demand of Turkey. The initial eight parameters were the gross domestic product (GDP) of Turkey, average annual US crude oil price (COP), inflation for Turkey in percentages (INF), the population of Turkey, total vehicle travel in kilometers for Turkey, total amount of goods transported on motorways, employment for Turkey, and trade of Turkey. However, after these eight parameters data are analyzed using Pearson and Spearman correlation methods, it is found out that five of these parameters are highly correlated. The remaining three parameters are the GDP of Turkey, COP, and INF for Turkey. Afterward, five separate scenarios are developed to forecast the future of the energy demand of Turkey. The first two scenarios involve the third- and fourth-order polynomial fitting, the third and fourth scenarios employ static and recurrent neural networks, and the fifth scenario utilizes autoregressive models to predict the future energy demand of Turkey. The efficient hybridization of the seagull optimization and very optimistic method of minimization metaheuristic algorithms is carried out to achieve the polynomial fitting of the data. The optimization performance of the hybrid algorithm is assessed by applying the algorithm on benchmark optimization problems and comparing the results with that of some other metaheuristic optimizers. Moreover, it is seen that the forecasts of the first scenario agree well with the Ministry of the Energy and Natural Resources estimates. [ABSTRACT FROM AUTHOR]

This research study aims to enhance the optimization accuracy of the two recently emerged metaheuristics of whale and sine–cosine optimizers by means of the balanced improvements in intensification and diversification phases of the algorithms provided by cellular automata (CA). Stagnation at the early phases of the iterations, which leads to entrapment in local optimum points in the search space, is one of the inherent drawbacks of the metaheuristic algorithms. As a favorable solution alternative to this problem, different types of cellular topologies are implemented into these two algorithms with a view to ameliorating their search mechanisms. Exploitation of the fertile areas in the search domain is maintained by the interaction between the topological neighbors, whereas the improved exploration is resulted from the smooth diffusion of the available population information among the structured neighbors. Numerical experiments have been carried out to assess the optimization performance of the proposed cellular-based algorithms. Optimization benchmark problems comprised of unimodal and multimodal test functions have been applied and numerical results have been compared with those found by some of the state-of-the-art literature optimizers including particle swarm optimization, differential evolution, artificial cooperative search and differential search. Cellular variants have been outperformed by the base algorithms for multimodal benchmark problems of Levy and Penalized1 functions. Then, the proposed cellular algorithms have been applied to two different parameter identification cases in order to test their efficiencies on real-world optimization problems. Extensive performance evaluations on different parameter optimization cases reveal that incorporating the CA concepts on these algorithms not only improves the optimization accuracy but also provides considerable robustness to acquired solutions. [ABSTRACT FROM AUTHOR]

This study investigates the Neural Network Predictive Control of a vapor compression cycle (VCC). VCC consists of four components, namely the compressor, electronic expansion valve (EEV), evaporator and condenser. Modeling of the compressor and EEV is carried out with the static relationships, while modeling of the evaporator and condenser is performed with the lumped parameter moving boundary method. The established thermodynamic model is validated against the ASPEN model with the same design specifications. The neural network is trained off-line with the input and output signal data of the established model. The solution of the optimization problem for the each time step is achieved with the metaheuristic method called Whale Optimization Algorithm in the predictive controller. Ultimately, performances of the four different controllers, namely the cooling load, first law efficiency, entropy generation and second law efficiency, are compared with each other. The results show that the entropy generation controller achieves the most favorable exergy destruction performance with 0.2% lower than the worst performer cooling load controller. It is also observed that the second law efficiency controller is the best performer in terms of the overall second law efficiency through the simulation time. [ABSTRACT FROM AUTHOR]

This 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. [ABSTRACT FROM AUTHOR]

This study aims to comparatively investigate the performance of a cascaded vapor compression absorption refrigeration system (CVCARS) operated with different refrigerants such as R1234yf, R134a, R717 and R290 in vapor compression cycle. Two design objectives are considered for performance evaluations. Total annual cost is the first design objective which includes investment and operational cost along with the social cost associated with carbon emissions. Exergy efficiency is the second considered objective which is related to thermodynamic issues. These problem objectives are individually and concurrently optimized by means of Artifical Cooperative Search metaheuristic algorithm and best results are compared for each cycle configuration. Single objective optimization results reveal that CVCARS working with R717 in vapor compression cycle has the lowest total annual cost whereas the maximum second law efficiency is obtained by the refrigeration system operated with R290 in vapor compression cycle. Following that, multi objective optimization is applied to acquire the Pareto optimal solutions which are nondominated to each other and no solution between them prevails over the other. Reputed decision making method TOPSIS is applied to choose the final best answer among the Pareto curve. It is seen that solution found by TOPSIS is skewed towards the minimum total annual cost and second law efficiency for each cycle configuration. Sensitivity analysis is then put into practice to observe the influences of variations of decision variables on design objectives as well as performance coefficients of the different cycles in the refrigeration system. [ABSTRACT FROM AUTHOR]

This study deals with the multi-objective optimization of basic and single-stage Organic Rankine Cycles (ORC) utilizing a low-grade heat source. Twelve different isentropic and dry pure refrigerants are considered as the primary working fluid for two different ORC configurations. Specific Investment Cost (SIC) and second law efficiency of the thermodynamic cycle are considered for optimization objectives to be optimized seperately and concurrently. Sixteen and twenty two different decision variables are respectively taken into account for modeling of the basic and the single-stage ORC optimization problems. The optimization problem is solved by applying a swarm based metaheuristic optimizer called Artificial Cooperative Search (ACS) algorithm. A pareto curve comprised of non dominated optimal solutions is constructed for each refrigerant-cycle pair and the best answer among the set of non dominated solutions are chosen by means of TOPSIS decision making method. Thermodynamic performance of each refrigerant are evaluated with respect to numerical outcomes of the objective functions. Comparative analysis based on the efficiencies of problem objectives reveals that R236ea, R245fa and R600 are selected as the best performers of the basic ORC and R245ca, R245fa and R600 are selected as the best performers of the single-stage ORC. Finally, a sensitivity analysis is executed to observe the effect of the decision variables on the objectives. It is understood that the evaporator shell diameter, number of tube passes in the evaporator, evaporator pressure and mass flow rate of the refrigerant are the decision variables with the most influence on the design objectives. [ABSTRACT FROM AUTHOR]

In this article, Artificial Cooperative Search (ACS) algorithm is incorporated with the quadratic approximation (QA) operator to solve the multi-objective economic emission load dispatch (EELD) problems with different generation units. ACS is a Swarm Intelligence-based metaheuristic algorithm, based on the interaction between prey and predator organisms in a habitat, which is effective at global search; however, it does not perform so well at exploring promising regions. The QA operator, on the other hand, is a non-derivative-based efficient local search method that finds the minimum of a quadratic hyperspace passing through three points in a D-dimensional space. Solving the EELD problems with the hybridized ACS-QA algorithm, as being proposed in the present article, leads to more accurate results with fewer function evaluations. Also, multi-objectivity of the problem is handled by transforming it into a single-objective problem by using the weighted sum method. The efficiency of the proposed ACS-QA algorithm is tested in comparison to the algorithms existing in literature by implementing it on six different benchmark optimization problems. Afterward, the proposed ACS-QA algorithm and the ACS algorithm are implemented on multi-objective EELD problems with different generation units. The results are compared with the solutions in literature utilizing different metaheuristic optimization algorithms. Both studies firmly showed that the ACS-QA algorithm is able to find more accurate results even though it uses fewer function evaluation calls. [ABSTRACT FROM AUTHOR]

Parameter estimation of chaotic systems is a challenging and critical topic in nonlinear science. Problem at hand is multi-dimensional and highly nonlinear thereof conventional optimization methods generally fail to extract the unknown parameters of chaotic system. In this study, Artificial Cooperative Search algorithm is put into practice for successful parameter estimation of chaotic systems and compared the parameter estimation performance of Artificial Cooperative Search with Bat, Artificial Bee Colony, Quantum behaved Particle Swarm Optimization algorithms. Parameter identification performance of each algorithm is outlined and benchmarked with several numerical simulations including Lörenz system, Duffing equation and Josephson junction. Results show that Artificial Cooperative Search algorithm outperforms other algorithms in terms of robustness and effectiveness. [ABSTRACT FROM AUTHOR]

This study proposes a novel chaotic quantum behaved particle swarm optimization algorithm for solving nonlinear system of equations. Different chaotic maps are introduced to enhance the effectiveness and robustness of the algorithm. Several benchmark studies are carried out. Logistic map gives the best results and is utilized in solving nonlinear equation sets. Nine well known problems are solved with our algorithm and results are compared with Quantum Behaved Particle Swarm Optimization, Intelligent Tuned Harmony Search, Gravitational Search Algorithm and literature studies. Comparison results reveal that the proposed algorithm can cope with the highly non-linear problems and outperforms many algorithms which exist in the literature. [ABSTRACT FROM AUTHOR]

• A new Master-Slave optimization algorithm is proposed. • Optimization performance of the algorithm is benchmarked against various optimizers. • The algorithm is applied to the long term operation of Aswan High Dam reservoir. • Predictive performance of the algorithm is validated against competing algorithms. Dams and reservoirs provide decision-makers and managers with appropriate control on the available water resources, allowing the implementation of various strategies for the most efficient usage of the available water resources. In areas where water supply exhibits significant temporal variation when compared with the demand, the challenge is to bridge the gap and achieve an optimal match between the water supply and demand patterns. Therefore, the release of water from reservoirs should be controlled to ensure that the operation rule for the available water storage in the reservoir is optimized to satisfy the future water demands. This level of optimal control can only be achieved using an efficient optimization algorithm to optimally derive the operation rule for such a complex water system. Herein, two main methods have been considered to tackle this water resource management problem. First, three different optimization algorithms, namely particle swarm optimization, differential evolution, and whale optimization algorithm, have been applied. In addition, two different optimization algorithms, namely crow search algorithm and master–slave algorithm, have been introduced to generate an optimal rule for water release policy. Further, the proposed optimization algorithms have been applied to one of the most critical dam and reservoir water systems, namely the Aswan High Dam (AHD), which controls almost 95% of Egypt's water resources. The current operation of AHD using the existing optimization rules resulted in a mismatch between the water supply and water demand. In other words, the water availability could be higher than the water demand during a certain period, whereas it could be less than the water demand during another period. The results denoted that the master–slave algorithm outperforms the remaining algorithms and generates an optimization rule that minimizes the mismatch between the water supply and water demand. [ABSTRACT FROM AUTHOR]