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Evolutionary algorithms show outstanding performance when they are applied to optimization problems with a few variables, i.e. problems with less than a hundred continuous variables. Large-scale global optimization with continuous variables is still a challenging task for a wide range of evolutionary algorithms. Their performance decreases when the number of variables increases because the search space grows exponentially. Classic evolutionary algorithms cannot find a good solution using the allocated resources. A cooperative coevolution approach is a good tool for increasing the performance of an optimizer in solving high-dimensional problems. The approach splits the objective vector into a predefined number of parts (subcomponents), and each part is optimized by its optimizer. This paper makes an effort to solve the problem of selecting the number of subcomponents. The paper represents a novel metaheuristic for solving optimization problems with a huge number of continuous variables. The suggested approach is based on the self-adaptive cooperation of algorithms and the cooperative coevolution approach. Each algorithm has a unique number of subcomponents. The metaheuristic automatically allocates resources between algorithms during the optimization process. Algorithms optimize the same population one by one. The proposed metaheuristic is titled COSACC, coordination of self-adaptive cooperative coevolution algorithms. We have evaluated the proposed algorithm on fifteen problems from the IEEE LSGO CEC'2013 benchmark. The study demonstrates that COSACC outperforms in average cooperative coevolution algorithms with the static number of subcomponents. Wilcoxon test has proven the results of numerical experiments. We have tested COSACC performance with other state-of-the-art metaheuristics, COSACC is a competitive approach. [ABSTRACT FROM AUTHOR]

In this paper, two new hybrid algorithms are proposed to solve global optimization problems. The first algorithm combines the sooty tern optimization algorithm with the arithmetic optimization algorithm, and the second algorithm combines the sooty tern optimization algorithm with the conjugate gradient method. The sooty tern optimization algorithm is a metaheuristic algorithm proposed by Dhiman and Kaur in 2019 Sooty migration and attack behaviors seabirds in nature were the primary inspiration for this algorithm. In this paper, this algorithm was hybridized using the arithmetic optimization algori thm and the conjugate gradient method, and the two algorithms were measured by applying them to 10 functions, and the results of the hybrid algorithms were very good compared to the original algorithm. [ABSTRACT FROM AUTHOR]

Filled function method is one of deterministic methods for solving global minimization problems. Filled function algorithm method generally contains of two main phases. First phase is to obtain local minimizer of objective function, second is to obtain minimizer or saddle point of filled function. In the second phase, vector direction plays an important role on finding stationary point of filled function, by assist in escaping from neighborhood of current minimizer of objective function of the first phase. In this paper, we introduce parameter free filled function and some typical vector direction to be applied in filled function algorithm. The algorithm method is implemented into some benchmark test functions. General computational and numerical results are presented to show the performance of each vector direction on filled function method for solving two dimensional unconstrained global optimization problems. [ABSTRACT FROM AUTHOR]

In energy management, providing accurate findings to anticipate electrical load consumption is critical. The goal of this study is to develop methods for predicting electrical load, including artificial intelligence, neural network, ARIMA models, Bayesian models, and regression models. This paper proposes a new support vector regression application with the chaotic algorithms for electrical load forecasting in the southern region (Basra, Maysan, Dhi Qar, Muthanna), When choosing three parameters of a model (SVR) using evolutionary algorithms, you often encounter problems of early convergence, slowly reaching the solution of global optimization or falling into local optimization, and to overcome early local optimization in determining three parameters of the model (SVR) a chaotic algorithm is used. The 1980-2019 electrical load was used as a data set, and the results (CGASVR) were compared with (CPSOSVR) and (CIASVR) to choose the best form of electrical load forecasting where results show that the CGASVR model is more superior and efficient based on MSE, MAE, MAPE and MPE. [ABSTRACT FROM AUTHOR]

In this paper, the global minimization problem of a multi-dimensional black-box Lipschitzian function is considered. In order to pass from the original Lipschitz multi-dimensional problem to a univariate one, an approach using space-filling curves to reduce the dimension is applied. The method does not use derivatives and, at each iteration, works with a set of estimates of the Hölder constant of the reduced one-dimensional problem. A two-phase technique is applied to accelerate the search of the global minimum. Numerical experiments carried out on several hundreds of test functions show a promising performance of the discussed algorithm in comparison with its direct competitors. [ABSTRACT FROM AUTHOR]

This paper presents a derivative-free nonmonotone hybrid tabu search to compute a solution of overdetermined systems of inequalities and equalities through the global optimization of an appropriate merit function. The proposed algorithm combines global and local searches aiming to reduce computational effort. Preliminary numerical results show the effectiveness of the combined heuristic. [ABSTRACT FROM AUTHOR]

This paper introduces a modification on the movement force vector of the Birbil and Fang’s electromagnetism-like algorithm [1] for solving global optimization problems with bounded variables. The proposed movement vector combines the total force exerted on each point of the population, at the current iteration, with the rate of change in the force vector of a previous iteration. Several widely used benchmark problems were solved to compare the proposed modification with the original algorithm. A performance profile assessment shows the efficiency and robustness of the proposed modification. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new global optimization algorithm for non-smooth unconstrained optimization problems. We construct a new global optimization (GO) method based on the function new smoothing technique. We give some numerical examples in order to demonstrate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

The emergence of mobile robot cannot be overemphasized as it is seen as essential but extreme technology taking into considerations, recent industrialisation. Automation has become a part of the process in order to obtain pathway of collision. The firefly algorithm has been identified to work on the principles of behaviour simulation. Two algorithms are implemented in this regard: Pseudo code of the basic firefly and Pseudo code of the firefly algorithm method that is proposed. To be considered for integration, the firefly algorithm method proposed is taken into consideration. A run of suggested mobile robot environment have been run. In conclusion, metaheuristic algorithms, is inspired by nature and are known as powerful tools especially in tackling global optimisation problem. [ABSTRACT FROM AUTHOR]

Solving global optimization problems plays a key role in most branches of natural sciences whilst dealing with everyday problems. With the help of optimization algorithms and the exponentially fast growth of the capabilities of the underlying hardware, the scale of feasible optimization tasks is reaching new levels. We help this goal with revisiting GLOBAL, a stochastic optimization method aiming to solve non-linear constrained optimization problems by the penalty function approach. It is a versatile tool for a broad range of problems, proven to be competitive in multiple comparisons. With the similarly rapid evolution of programming habits and tools, time naturally passed by GLOBAL’s latest implementation so that it became a drawback. Now, we present GlobalJ, a fully modularized Java framework refurbishing and extending the potential of this algorithm. [ABSTRACT FROM AUTHOR]

Univariate box-constrained Lipschitz global optimization problems are considered in this contribution. Geometric and information statistical approaches are presented. The novel powerful local tuning and local improvement techniques are described in the contribution as well as the traditional ways to estimate the Lipschitz constant. The advantages of the presented local tuning and local improvement techniques are demonstrated using the operational characteristics approach for comparing deterministic global optimization algorithms on the class of 100 widely used test functions. [ABSTRACT FROM AUTHOR]

We propose a new method for solving univariate global optimization problems by combining a lower bound function given in (αBB) method [1] [For more details (Algorithms,Methods) one see [1]], with the improved lower bound function of the method developed in [2]. The new lower bound function is better than the two lower bound functions by its construction. The complementarity of the two lower bound functions allows us to derive the convex/concave test and the pruning step which accelerate the convergence of the proposed method. Illustrative examples are treated efficiently. [ABSTRACT FROM AUTHOR]

The herein presented mutation-based artificial fish swarm (AFS) algorithm includes mutation operators to prevent the algorithm to falling into local solutions, diversifying the search, and to accelerate convergence to the global optima. Three mutation strategies are introduced into the AFS algorithm to define the trial points that emerge from random, leaping and searching behaviors. Computational results show that the new algorithm outperforms other well-known global stochastic solution methods. [ABSTRACT FROM AUTHOR]

In this work, the Penalty Stretched Simulated Annealing Method and the Multilocal Branch and Bound method were applied to identify the stationary points of the tangent plane distance function defined for the Gibbs energy. The classic excess Gibbs energy Non Random Two Liquid model was used for these studies in several multicomponent mixtures, for which specific numerical difficulties were shown. The preliminary results obtained by applying the methodology developed in this work were satisfactory. [ABSTRACT FROM AUTHOR]