scholarly journals Improving optimization using adaptive algorithms

2021 ◽  
Vol 16 (1) ◽  
pp. 14-18
Author(s):  
László Kota ◽  
Károly Jármai
Keyword(s):  
Optimization Problems ◽  
Optimization Technique ◽  
Optimization Methods ◽  
Adaptive Methods ◽  
Optimization Method ◽  
Fine Tuning ◽  
Logistics Industry ◽  
Highly Nonlinear ◽  
Industrial Projects ◽  
Suboptimal Solution

AbstractIn the research projects and industrial projects severe optimization problems can be met, where the number of variables is high, there are a lot of constraints, and they are highly nonlinear and mostly discrete issues, where the running time can be calculated sometimes in weeks with the usual optimization methods on an average computer. In most cases in the logistics industry, the most robust constraint is the time. The optimizations are running on a typical office configuration, and the company accepts the suboptimal solution what the optimization method gives within the appropriate time limit. That is, why adaptivity is needed. The adaptivity of the optimization technique includes parameters of fine-tuning. On this way, the most sensitive setting can be found. In this article, some additional adaptive methods for logistic problems have been investigated to increase the effectivity, improve the solution in a strict time condition.

Application of a Multilevel Firefly Algorithm on a Large Variable Number Logistic Problem

2019 ◽  
Vol 13 (2) ◽  
pp. 21-28
Author(s):  
László Kota ◽  
Károly Jármai
Keyword(s):  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Variable Number ◽  
Multilevel Method ◽  
Logistic Problem ◽  
Industrial Projects ◽  
Suboptimal Solution ◽  
Discrete Problems ◽  
Chain Problem

During our research and industrial projects, we often meet difficult optimization problems, a lot of variables, a lot of constraints, nonlinear and mostly discrete problems, where the running time can be calculated sometimes in weeks with the usual optimization methods on an average computer. In the most cases in the logistic industry the strongest constraint is the time. The optimizations are running on a usual office configuration and the company accepts the suboptimal solution what the optimization method gives in the appropriate time limit. In this article we will investigate a multilevel method on supply chain problem, to increase the effectivity, improve the solution in a strict time condition.

A New Structural Optimization Method Based on Group Hunting of Animals: Hunting Search (HuS)

2010 ◽  
Author(s):  
R. Oftadeh ◽  
M. J. Mahjoob
Keyword(s):  
Structural Optimization ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Harmony Search ◽  
Optimization Methods ◽  
Optimization Method ◽  
Global Optimization Methods ◽  
Multi Level ◽  
Group Hunting

This paper presents a novel structural optimization algorithm based on group hunting of animals such as lions, wolves, and dolphins. Although these hunters have differences in the way of hunting but they are common in that all of them look for a prey in a group. The hunters encircle the prey and gradually tighten the ring of siege until they catch the prey. In addition, each member of the group corrects its position based on its own position and the position of other members. If the prey escapes from the ring, the hunters reorganize the group to siege the prey again. A benchmark numerical optimization problems is presented to show how the algorithm works. Three benchmark structural optimization problems are also presented to demonstrate the effectiveness and robustness of the proposed Hunting Search (HuS) algorithm for structural optimization. The objective in these problems is to minimize the weight of bar trusses. Both sizing and layout optimization variables are included, too. The proposed algorithm is compared with other global optimization methods such as CMLPSA (Corrected Multi-Level & Multi-Point Simulated Annealing) and HS (Harmony Search). The results indicate that the proposed method is a powerful search and optimization technique. It yields comparable and in some cases, better solutions compared to those obtained using current algorithms when applied to structural optimization problems.

A Multi-Stage Krill Herd Algorithm for Global Numerical Optimization

2016 ◽  
Vol 25 (02) ◽  
pp. 1550030 ◽  
Author(s):  
Gai-Ge Wang ◽  
Amir H. Gandomi ◽  
Amir H. Alavi ◽  
Suash Deb
Keyword(s):  
Numerical Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Fine Tuning ◽  
Benchmark Problems ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Multi Stage ◽  
Krill Herd

A multi-stage krill herd (MSKH) algorithm is presented to fully exploit the global and local search abilities of the standard krill herd (KH) optimization method. The proposed method involves exploration and exploitation stages. The exploration stage uses the basic KH algorithm to select a good candidate solution set. This phase is followed by fine-tuning a good candidate solution in the exploitation stage with a focused local mutation and crossover (LMC) operator in order to enhance the reliability of the method for solving global numerical optimization problems. Moreover, the elitism scheme is introduced into the MSKH method to guarantee the best solution. The performance of MSKH is verified using twenty-five standard and rotated and shifted benchmark problems. The results show the superiority of the proposed algorithm to the standard KH and other well-known optimization methods.

Optimization Design of Trusses Based on Covariance Matrix Adaptation Evolution Strategy Algorithm

2012 ◽  
Vol 215-216 ◽  
pp. 133-137
Author(s):  
Guo Shao Su ◽  
Yan Zhang ◽  
Zhen Xing Wu ◽  
Liu Bin Yan
Keyword(s):  
Stochastic Optimization ◽  
Covariance Matrix ◽  
Optimization Problems ◽  
Fitness Function ◽  
Optimization Methods ◽  
Evolution Strategy ◽  
Covariance Matrix Adaptation ◽  
Study Results ◽  
Highly Nonlinear ◽  
Adaptation Evolution

Covariance matrix adaptation evolution strategy algorithm (CMA-ES) is a newly evolution algorithm. It has become a powerful tool for solving highly nonlinear multi-peak optimization problems. In many real-world optimization problems, the location of multiple optima is often required in a search space. In order to evaluate the solution, thousands of fitness function evaluations are involved that is a time consuming or expensive processes. Therefore, conventional stochastic optimization methods meet a special challenge for a very large number of problem function evaluations. Aiming to overcome the shortcoming of stochastic optimization methods in the high calculation cost, a truss optimal method based on CMA-ES algorithm is proposed and applied to solve the section and shape optimization problems of trusses. The study results show that the method is feasible and has the advantages of high accuracy, high efficiency and easy implementation.

Study on Optimal Design of a Folding-Type Hatch Cover Considering Material Selection

2021 ◽  
pp. 1-11
Author(s):  
Gerry Liston Putra ◽  
Mitsuru Kitamura ◽  
Akihiro Takezawa
Keyword(s):  
Production Systems ◽  
Material Selection ◽  
Optimization Technique ◽  
Plate Thickness ◽  
Optimization Methods ◽  
Optimization Method ◽  
Production Costs ◽  
Plate Material ◽  
Material Type ◽  
Material Cost

Abstract Most shipyard companies maintain efficiency in all aspects of their business to survive. One of these aspects is ship production costs and their reduction. This study proposes a solution to this problem using an optimization method. A hatch cover composed of plates and stiffeners was selected as a case study. In this study, the mass and material cost of the hatch cover was optimized as an objective function using the Pareto approach with developed optimization methods. Plate thickness t, stiffener shape s, and plate material type m were selected as the design variables in this study along with some constraints. To estimate the optimal plate thickness, an expression of stress equations was Developed using an optimization technique. Furthermore, stiffener shape and plate material type selection were optimized using a genetic algorithm (GA). The results show that the optimization method is effective to decrease the mass and material cost of a hatch cover. Introduction The demand for new shipbuilding has decreased because of the effect of the economic crisis that hit almost every country in the world. Shipyard companies must think innovatively and creatively to survive under the pressure of this crisis by evaluating various studies and improvising new methods to achieve efficiency. One of the studies that has been performed examines the methods to reduce the fabrication cost of ship structures to stay profitable through the optimization of work hours, workflow production systems, and structural design.

Design of Optimal CMOS Analog Amplifier Circuits Using a Hybrid Evolutionary Optimization Technique

2017 ◽  
Vol 27 (02) ◽  
pp. 1850029 ◽  
Author(s):  
Bishnu Prasad De ◽  
Kanchan Baran Maji ◽  
Rajib Kar ◽  
Durbadal Mandal ◽  
Sakti Prasad Ghoshal
Keyword(s):  
Power Dissipation ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Optimization Method ◽  
Vlsi Circuits ◽  
Optimal Designs ◽  
Efficient Design ◽  
Mos Transistors ◽  
Evolutionary Technique ◽  
Random Particle

This paper proposes an efficient design technique for two commonly used VLSI circuits, namely, CMOS current mirror load-based differential amplifier circuit and CMOS two-stage operational amplifier. The hybrid evolutionary method utilized for these optimal designs is random particle swarm optimization with differential evolution (RPSODE). Random PSO utilizes the weighted particles for monitoring the search directions. DE is a robust evolutionary technique. It has demonstrated an exclusive performance for the optimization problems which are continuous and global but suffers from the uncertainty issues. PSO is a robust optimization method but suffers from sub-optimality problem. This paper effectively hybridizes the random PSO and DE to remove the limitations related to both the techniques individually. In this paper, RPSODE is employed to optimize the sizes of the MOS transistors to reduce the overall area taken by the circuit while satisfying the design constraints. The results obtained from RPSODE technique are validated in SPICE environment. SPICE-based simulation results justify that RPSODE is a much better technique than other formerly reported methods for the designs of the above mentioned circuits in terms of MOS area, gain, power dissipation, etc.

Non-Probabilistic Based Topology Optimization Under External Load Uncertainty With Eigenvalue-Superposition of Convex Models

2013 ◽  
Author(s):  
Xike Zhao ◽  
Hae Chang Gea ◽  
Wei Song
Keyword(s):  
Topology Optimization ◽  
External Load ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Convex Model ◽  
Structure Response ◽  
Gradient Based ◽  
Bounded Convex ◽  
Topology Optimization Method

In this paper the Eigenvalue-Superposition of Convex Models (ESCM) based topology optimization method for solving topology optimization problems under external load uncertainties is presented. The load uncertainties are formulated using the non-probabilistic based unknown-but-bounded convex model. The sensitivities are derived and the problem is solved using gradient based algorithm. The proposed ESCM based method yields the material distribution which would optimize the worst structure response under the uncertain loads. Comparing to the deterministic based topology optimization formulation the ESCM based method provided more reasonable solutions when load uncertainties were involved. The simplicity, efficiency and versatility of the proposed ESCM based topology optimization method can be considered as a supplement to the sophisticated reliability based topology optimization methods.

A Study of Parallel Efficiency of Modified Direct Algorithm Applied to Thermohydrodynamic Lubrication

2009 ◽  
Vol 25 (2) ◽  
pp. 143-150 ◽  
Author(s):  
N. Wang ◽  
C.-M. Tsai ◽  
K.-C. Cha
Keyword(s):  
Parallel Computing ◽  
Optimization Problems ◽  
Load Capacity ◽  
Optimization Methods ◽  
Optimization Method ◽  
Parallel Optimization ◽  
Direct Algorithm ◽  
Slider Bearing ◽  
Parallel Efficiency ◽  
Fine Grain

AbstractThis study examines the parallel computing as a means to minimize the execution time in the optimization applied to thermohydrodynamic (THD) lubrication. The objective of the optimization is to maximize the load capacity of a slider bearing with two design variables. A global optimization method, DIviding RECTangle (DIRECT) algorithm, is used. The first approach was to apply the parallel computing within the THD model in a shared-memory processing (SMP) environment to examine the parallel efficiency of fine-grain computation. Next, a distributed parallel computing in the search level was conducted by use of the standard DIRECT algorithm. Then, the algorithm is modified to provide a version suitable for effective parallel computing. In the latter coarse-grain computation the speedups obtained by the DIRECT algorithms are compared with some previous studies using other parallel optimization methods. In the fine-grain computation of the SMP machine, the communication and overhead time costs prohibit high speedup in the cases of four or more simultaneous threads. It is found that the standard DIRECT algorithm is an efficient sequential but less parallel-computing-friendly method. When the modified algorithm is used in the slider bearing optimization, a parallel efficiency of 96.3% is obtained in the 16-computing-node cluster. This study presents the modified DIRECT algorithm, an efficient parallel search method, for general engineering optimization problems.

scholarly journals An Improved Subgradiend Optimization Technique for Solving IPs with Lagrangean Relaxation

2013 ◽  
Vol 61 (2) ◽  
pp. 135-140
Author(s):  
M Babul Hasan ◽  
Md Toha
Keyword(s):  
Dual Problem ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Concave Function ◽  
Optimization Method ◽  
Duality Gap ◽  
Dual Function ◽  
Subgradient Optimization ◽  
Step Size ◽  
Primal Problem

The objective of this paper is to improve the subgradient optimization method which is used to solve non-differentiable optimization problems in the Lagrangian dual problem. One of the main drawbacks of the subgradient method is the tuning process to determine the sequence of step-lengths to update successive iterates. In this paper, we propose a modified subgradient optimization method with various step size rules to compute a tuning- free subgradient step-length that is geometrically motivated and algebraically deduced. It is well known that the dual function is a concave function over its domain (regardless of the structure of the cost and constraints of the primal problem), but not necessarily differentiable. We solve the dual problem whenever it is easier to solve than the primal problem with no duality gap. However, even if there is a duality gap the solution of the dual problem provides a lower bound to the primal optimum that can be useful in combinatorial optimization. Numerical examples are illustrated to demonstrate the method. DOI: http://dx.doi.org/10.3329/dujs.v61i2.17059 Dhaka Univ. J. Sci. 61(2): 135-140, 2013 (July)

Genetic Algorithms for Optimization of Building Envelopes and the Design and Control of HVAC Systems

2003 ◽  
Vol 125 (3) ◽  
pp. 343-351 ◽  
Author(s):  
L. G. Caldas ◽  
L. K. Norford
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Hvac Systems ◽  
Design Problems ◽  
Building Walls ◽  
Building Form ◽  
And Control ◽  
Future Work

Many design problems related to buildings involve minimizing capital and operating costs while providing acceptable service. Genetic algorithms (GAs) are an optimization method that has been applied to these problems. GAs are easily configured, an advantage that often compensates for a sacrifice in performance relative to optimization methods selected specifically for a given problem, and have been shown to give solutions where other methods cannot. This paper reviews the basics of GAs, emphasizing multi-objective optimization problems. It then presents several applications, including determining the size and placement of windows and the composition of building walls, the generation of building form, and the design and operation of HVAC systems. Future work is identified, notably interfaces between a GA and both simulation and CAD programs.

Sign in / Sign up

Export Citation Format

Share Document