Search

Optimizing the heliostat field aiming strategy is crucial for maximizing thermal power production in solar power tower (SPT) plants while adhering to operational constraints. Although existing approaches can yield highly optimal solutions, their considerable computational cost makes them unsuitable for real-time optimization in large-scale scenes. This study introduces an efficient, intelligent, real-time optimization method based on a meta-heuristic algorithm to effectively and reliably manage SPT plant operations under varying solar conditions, such as cloud shadowing variations. To minimize redundant calculations, the real-time optimization problem is framed in a way that captures the operational continuity of the heliostat, which can be utilized to streamline the solution process. The proposed method is tested in a simulation environment that includes a heliostat field, cylindrical receiver, and cloud movement model. The results demonstrate that the algorithm presented in this paper offers higher intercept efficiency, improved robustness, and reduced optimization time in more complex scenes. [ABSTRACT FROM AUTHOR]

Four-bar linkages are one of the most widely used mechanisms in industries. This paper presents a comparative study on the accuracy and efficiency of the optimum synthesis of path-generating four-bar linkages using five metaheuristic optimization algorithms. The utilized metaheuristic methods included two swarm intelligence-based algorithms, i.e., particle swarm optimization and hybrid particle swarm optimization, and three evolutionary-based algorithms, i.e., differential evolution, ensemble of parameters and mutation strategies in differential evolution, and linearly ensemble of parameters and mutation strategies in differential evolution. The objective function to be minimized is the sum of squares of the distance between the generated points and the precision points of a coupler point. The optimal design of four-bar linkages must meet the Grashof's criteria and exhibit sequential constraints that can prevent the occurrence of order defect. This study investigated five representative cases of the dimensional synthesis of four-bar path generators with and without prescribed timing and compared the optimal solutions of the utilized five metaheuristic methods to those of previously reported algorithms in literature. The improved metaheuristic methods exhibited superior optimal solution and enhanced reliability compared to the original methods. Moreover, three improved metaheuristic methods were not only easy implemented, but also more efficient for solving the optimal synthesis problems, particularly for high dimensional problems. [ABSTRACT FROM AUTHOR]

Everyday, a large number of complex scientific and industrial problems involve finding an optimal solution in a large solution space. A challenging task for several optimizations is not only the combinatorial operation but also the constraints of available devices. This paper proposes a novel optimization algorithm, namely the compact bat algorithm (cBA), to use for the class of optimization problems involving devices which have limited hardware resources. A real-valued prototype vector is used for the probabilistic operations to generate each candidate for the solution of the optimization of the cBA. The proposed cBA is extensively evaluated on several continuous multimodal functions as well as the unequal clustering of wireless sensor network (uWSN) problems. Experimental results demonstrate that the proposed algorithm achieves an effective way to use limited memory devices and provides competitive results. [ABSTRACT FROM AUTHOR]

In this work, the applicability of a Firefly Algorithm (FA) to the real problem of the minimum cost of a detailed design for steel frames is studied. To reduce the calculation time, which is a common problem of meta-heuristic algorithms when they are used to solve real design cases, and to better suit the characteristics of the algorithm, a parallel migration strategy has been implemented and tested. As it is well known that the performance of any metaheuristic algorithm depends on the chosen value of its parameters, an extensive sensitivity analysis has been carried out. This not only serves to improve performance but also provides information on how it depends on the values of these parameters. With the information obtained from this analysis, and in order to achieve the robust behavior of the algorithm, a parameter control strategy has also been implemented and tested. Finally, a study demonstrating the close dependence between one of the parameters and the number of variables considered in the examples has been carried out. As a result of this final study, a simple expression is proposed that provides the minimum necessary population based on the number of variables in the problem. [ABSTRACT FROM AUTHOR]

The subject of the study was an application of nature-inspired metaheuristic algorithms to node configuration optimization in optical networks. The main objective of the optimization was to minimize capital expenditure, which includes the costs of optical node resources, such as transponders and amplifiers used in a new generation of optical networks. For this purpose a model that takes into account the physical phenomena in the optical network is proposed. Selected nature-inspired metaheuristic algorithms were implemented and compared with a reference, deterministic algorithm, based on linear integer programming. For the cases studied the obtained results show that there is a large advantage in using metaheuristic algorithms. In particular, the evolutionary algorithm, the bees algorithm and the harmony search algorithm showed superior performance for the considered data-sets corresponding to large optical networks; the integer programming-based algorithm failed to find an acceptable sub-optimal solution within the assumed maximum computational time. All optimization methods were compared for selected instances of realistic teletransmission networks of different dimensions subject to traffic demand sets extracted from real traffic data. [ABSTRACT FROM AUTHOR]