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In this paper, a modified Pascoletti–Serafini scalarization approach, called MOP_MPS, is proposed to generate approximations of a Pareto front of bounded multi-objective optimization problems (MOPs). The objective is obtaining some points with an almost even distribution overall Pareto front. This algorithm is applied to six test problems with convex, non-convex, connected, and dis-connected Pareto fronts, and its results are compared with results of some famous algorithms. The results emphasize that MOP_MPS is effective and competitive in comparing with the other considered algorithms. In addition, it is shown that an optimal solution of a multiplicative programming problem is a properly Pareto optimal solution of an MOP. By considering this relation between MOPs and multiplicative programming problems (MPPs), another algorithm based on MOP_MPS, called MPP_MPS, is suggested for approximately solving non-linear MPPs in which functions multiplied are continuous and bounded from below. The computational results on seven problems of convex MPPs demonstrate that the algorithm is much better than a cut and bound algorithm presented by Shao and Ehrgott in terms of CPU time. [ABSTRACT FROM AUTHOR]

This paper introduces a distributed compensation approach for the global optimization with separable objective functions and coupled constraints. By employing compensation variables, the global optimization problem can be solved without the information exchange of coupled constraints. The convergence analysis of the proposed algorithm is presented with the convergence condition through which a diminishing step-size with an upper bound can be determined. The convergence rate can be achieved at $O({lnT}/{\sqrt {T}})$. Moreover, the equilibrium of this algorithm is proved to converge at the optimal solution of the global optimization problem. The effectiveness and the practicability of the proposed algorithm is demonstrated by the parameter optimization problem in smart building. [ABSTRACT FROM AUTHOR]

Focused on the problems that low-precision and easily to fall into local optimum result in the high-dimensional space by the longhorn search algorithm,this paper proposed a new optimization algorithm of the short-lived beetles-the shortlived search algorithm based on quadratic interpolation which was called QIBAS.The algorithm took tentacle’s left and right tentacles as interpolation coordinate points after the beetle moved.And it generated new solution with a second interpolation,then compared to the current optimal solution and the global optimal solution with the fitness of interpolation solution.At the same time to update the global optimal solution,it performed numerical simulation tests on multiple unimodal and multimodal functions,and their dimensions took as 100,500,1 000,5 000 and 10 000.The simulation results show that the introduction of quadratic interpolation effectively improves the ability of the BAS algorithm to jump out of the local optimum.When QIBAS solves the optimal value,its solution accuracy is greatly improved,and the convergence speed is also significantly improved.The effectiveness of the improved algorithm is verified. [ABSTRACT FROM AUTHOR]

In this work, a novel metaheuristic optimization algorithm called Special Relativity Search (SRS) is proposed. The SRS is inspired by the interaction of particles in an electromagnetic field. Particle interactions are calculated using the Lorentz force, and the equation of motion is developed using angular frequency. The magnetic force between particles is perpendicular to the velocity of charged particles and the magnetic field, which causes particles to move in a circular trajectory. In this method, for the first time, the theory of special relativity physics is utilized to determine the coordinates of charged particles in each rotation. The SRS main step equation is developed using the two phenomena of length contraction and time dilation. Charged particles are members of the initial population that is randomly generated, and their charge is determined based on their fitness. To show the efficiency and robustness of the SRS in solving optimization problems, 83 benchmark functions, which are a wide range of mathematical problems, are selected and optimized based on the values of Best, Mean, Median, and Standard Deviation (SD). The statistical test of the Wilcoxon Signed Ranks (WSR) is performed to fairly compare the results of this new method with other popular metaheuristic algorithms. The test results show that in most cases SRS is superior to other methods from the state-of-the-art. The results of evaluated optimization problems show that SRS is more effective and efficient compared to some other well-known metaheuristic methods in solving optimization problems. [ABSTRACT FROM AUTHOR]

To avoid the problem of traditional genetic algorithm (GA) and particle swarm optimization (PSO) trapped into local minimum, we propose new hybrid approach using PSO and GA applied to discrete traveling salesman problem (TSP). Thus, constructed hybrid algorithm with distribution of neighbours in known TSP instances is taken into account. Our experimental results indicate a few advantages of the method used over the standard ones, for example better convergence. The algorithm is effective and does not depend so strongly on initial conditions, as often happens. [ABSTRACT FROM AUTHOR]

This paper presents a discussion of different approaches for globally optimizing the objective functions defined in a standard continuous search space when passing to a discrete respective problems. The related techniques are developed in the context of the particle swarm optimization method (PSO). As far as PSO has shown recently a good effectiveness in performing difficult optimization tasks we intend to approve its applicability to discrete systems, i.e. traveling salesman problem (TSP), being well known representative and still most difficult discrete task between all NP-hard problems. To do this we can follow two roads: firstly redefine discrete system in order to use PSO and secondly adjust PSO, using genetic operators, for discrete system. [ABSTRACT FROM AUTHOR]

[Display omitted] • Overview of the adjoint method for inverse regulation of indoor environment. • Single method has limited ability to improve the efficiency of inverse identification. • The coupling of multiple methods would be the best choice to improve efficiency. • Most researchers tend to broaden the application scope of adjoint method. • The combination with other methods is the trend of finding the global optimum. The adjoint method has been used in the indoor environment for more than ten years, but it has not been widely used up to now. This paper describes the current state of the adjoint method and presents an overview of the adjoint method for inverse regulation of the indoor environment from different aspects, including the inverse identification accuracy, inverse identification efficiency, inverse identification function, and globally optimal. The adjoint turbulence model could improve the accuracy of inverse identification, but its efficiency is lower than the 'frozen turbulence' assumption. The methods used to improve the efficiency of inverse identification includes multi-zone model, fast fluid dynamics (FFD), coarse grid, zero-equation eddy-viscosity models, sub-iteration method, adaptive step size, parallelization technology, and so on. However, a single method has limited ability to improve the efficiency of inverse identification, and the coupling of multiple methods would be the best choice. For the inverse identification function aspect, statistics show that more than 64% of researchers tend to broaden the application scope of the original adjoint method. Among them, the probability-based adjoint method was developed to determine the airborne contaminant source location and the finite element method (FEM) and topology method were used to identify the location, number, size, shape of the inlets. In addition, since the adjoint method has inherent defects in finding the global optimum, and the combination with other methods is the trend of finding the global optimum. Although the adjoint method has made great progress in different aspects, manual derivation of the adjoint equations and development of solvers are the main obstacles for many new researchers in the field of the indoor environment. So, user-friendly methods or solvers need further consideration to facilitate the popularization of this method. [ABSTRACT FROM AUTHOR]

Highlights: [•] MACS and ADE algorithms are hybridized as MACS-ADE method for solving STHGS problem. [•] An adaptive mutation is integrated into the proposed algorithm to avoid premature convergence. [•] MACS and ADE are run in parallel in search of better solution. [•] Several effective heuristic strategies are designed for dealing with various constraints of STHGS problem. [ABSTRACT FROM AUTHOR]

Abstract: The binary-coded elitist non-dominated sorting genetic algorithm with the modified jumping gene operator (NSGA-II-mJG) is used to obtain global optimal solutions of flotation circuits. Several single-objective and multi-objective optimization problems are solved using the interconnecting cell linkage parameters (fraction flow rates) and the mean cell residence times as the decision variables. In the single-objective problem, the overall recovery of the concentrate stream is maximized for a desired grade of the concentrate. Two two-objective optimization problems are then solved. In one, the number of non-linking streams and the overall recovery of the concentrate are maximized simultaneously. This gives several simple circuits in a systematic manner with only marginally lower recoveries. In the other two-objective optimization problem, the overall recovery of the concentrate is maximized while the total cell volume is minimized. A three-objective problem (maximization of the overall recovery of the concentrate, maximization of the number of non-linking streams and minimization of the total cell volume) is then solved. All the problems constrain the grade of the product to lie at a fixed value. Finally, a complex and computationally intensive four-objective optimization problem is solved. The solution of several practical optimization problems in this study helps develop useful insights into the optimal solutions. [Copyright &y& Elsevier]