Different Types Of Optimization Problems Research Articles

DEVELOPMENT OF PROJECTS PORTFOLIO OF HIGH-TECH ENTERPRISE

The rational use of expensive high-tech enterprise resources (financial, staff, technology) required formation of the appropriated project portfolio. In this article the mathematical model of the prior formation of a project portfolio of a high-tech enterprise with the available nomenclature of products and potential customers is proposed. The mathematical model of the optimal portfolio formation of high-tech enterprise Zorya-Mashproekt within available products (gas turbines), customers, and the cost of supplies in NEOS Server is calculated. NEOS Server provides the interaction between users and systems to solve many different types of optimization problems. Users download model file and data file in the language AMPL to the server which are then processed in different systems (KNITRO, MINTO, BARON, ASA, etc.). In the article the mathematical model and the input data are described in the language AMPL. The problem using MINTO NEOS-interface is solved.

Optimization of Cold-Formed Channel Sections Using Imperialist Competitive Algorithm

The Imperialist Competitive Algorithm (ICA) is a novel computational method based on the concept of socio-political motivated strategy, which is usually used to solve different types of optimization problems. This paper presents the optimization of cold-formed channel section subjected to axial compression force utilizing the ICA method. The results are then compared to the Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) algorithm for validation purpose. The results obtained from the ICA method is in good agreement with the GA and SQP method in terms of weight but slightly different in the geometry shape.

Accelerating fuzzy genetic algorithm for the optimization of steel structures

In this paper, the combination of a genetic algorithm and fuzzy logic is used for different types of optimization problems (sizing, topology and geometry) in steel structures. The primary objective is to introduce a new fitness function that uses a real value for the overall satisfaction parameter. By considering a real value instead of a random value for the overall satisfaction parameter, the probability of reaching the global optimum increases. In addition, to decrease the Computation time and to accelerate the convergence rate in the optimization process, a penalty function is added to the fitness function. Also, in spite of the previous methods, fuzzy GA is applied from the first generation of optimization problems. Furthermore, for this process, a similar bilinear membership function is used for the objective function and the constraints. The efficiency and robustness of the proposed approach is demonstrated by several illustrative examples and the results are compared to those of previous studies.

Clan particle swarm optimization

PurposeParticle swarm optimization (PSO) has been used to solve many different types of optimization problems. In spite of this, the original version of PSO is not capable to find reasonable solutions for some types of problems. Therefore, novel approaches to deal with more sophisticated problems are required. Many variations of the basic PSO form have been explored, targeting the velocity update equation. Other approaches attempt to change the communication topology inside the swarm. The purpose of this paper is to propose a topology based on the concept of clans.Design/methodology/approachFirst of all, this paper presents a detailed description of its proposal. After that, it shows a graphical convergence analysis for the Rosenbrock benchmark function. In the sequence, a convergence analysis for clan PSO with different parameters is performed. A comparison with star, ring, focal, von Neumann and four clusters topologies is also performed.FindingsThe paper's simulation results have shown that the proposal obtained better results than the other topologies for the benchmark functions selected for this paper.Originality/valueThe proposed topology for PSO based on clans provides a novel form for information distribution inside the swarm. In this approach, the topology is determined dynamically during the search process, according to the success rate inside each clan.

Loney's Solenoid Design Using an Artificial Immune Network With Local Search Based on the Simplex Method

The use of automatic optimization procedures for designing electromagnetic devices is becoming more and more common. Many of these problems are described by nonlinear relationships, which introduce the possibility of multiple local minima. Artificial immune systems are learning and optimization methods that can be applied to the solution of many different types of optimization problems in electromagnetics. In this paper, the shape design of Loney's solenoid benchmark problem is carried out by an optimization method (opt-aiNet) inspired by an artificial immune network which is combined with a local search (Nelder-Mead simplex search method). Comparisons between the results obtained by opt-aiNet and opt-aiNet with local search are reported and commented.

Dynamic programming and parallel computers

The computational theory of dynamic programming is examined from the viewpoint of parallel computation. A discussion of various forms of parallelism, the corresponding parallel algorithms, the applicability of the algorithms to different types of optimization problems, and their advantages over serial computation is presented. In addition, parallel aspects of various dimensionality reduction techniques such as state increment dynamic programming, successive approximations, and shift vectors are also given.