Determination of the Effects of Penalty Coefficient on the Meta-Heuristic Optimization Process

Purpose This paper aims to present a new multi-community meta-heuristic optimization algorithm, which is called shuffled shepherd optimization algorithm (SSOA). In this algorithm. Design/methodology/approach The agents are first separated into multi-communities and the optimization process is then performed mimicking the behavior of a shepherd in nature operating on each community. Findings A new multi-community meta-heuristic optimization algorithm called a shuffled shepherd optimization algorithm is developed in this paper and applied to some attractive examples. Originality/value A new metaheuristic is presented and tested with some classic benchmark problems and some attractive structures are optimized.

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An expert assistant for selecting green splits, cycle times, and phasing schemes based on the Canadian Capacity Guide procedures

Canadian Journal of Civil Engineering ◽

10.1139/l92-030 ◽

1992 ◽

Vol 19 (2) ◽

pp. 245-251 ◽

Cited By ~ 2

Author(s):

J. Allen Stewart ◽

Michel Van Aerde

Keyword(s):

Key Words ◽

Cycle Time ◽

Traffic Signal ◽

Optimization Process ◽

Signal Timing ◽

Cycle Times ◽

Ultimate Outcome

The determination of the optimum phase scheme, cycle time, and green split for an isolated traffic signal is usually a very tedious and time-consuming activity. Several computer packages, such as the Micro-Sintral program, have been developed to assist in such situations by automating many of the calculations for a given set of inputs. Unfortunately, the inputs are often equally difficult to determine, and as they may depend upon the ultimate outcome of the optimization process, several time-consuming iterations, during which new input values must be calculated, are required. This paper presents a program called Signal Expert which automates the above iterations and therefore greatly reduces the traffic engineer's efforts in generating a new signal timing plan based on the rather sophisticated procedures that are outlined in the Canadian Capacity Guide. The benefits of applying this new program are illustrated and discussed using the example intersections that are analyzed in the Canadian Capacity Guide and in the Micro-Sintral User's Manual. Key words: isolated intersections, capacity, delay.

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Manipulation of Uncertainties in the Determination of Optimal Machining Conditions Under Multiple Criteria

Journal of Manufacturing Science and Engineering ◽

10.1115/1.2831094 ◽

1997 ◽

Vol 119 (2) ◽

pp. 186-192 ◽

Cited By ~ 7

Author(s):

Li Chen ◽

S. S. Rao

Keyword(s):

Experimental Data ◽

Traditional Approach ◽

Fuzzy Model ◽

Computational Approach ◽

Optimization Process ◽

Empirical Equations ◽

Computational Optimization ◽

Machining Conditions ◽

Degree Of Certainty

In any real-world manufacturing situation, the problem of determining the optimum machining conditions involves not only empirical data but also imprecise information. Uncertain factors may need to be considered in the computational optimization process due to fuzziness present in the empirical equations and experimental data used. To manipulate the uncertainties in the optimization process, a fuzzy model is introduced and investigated. The fuzzy model quantifies the degree of certainty (or uncertainty) in the range 0 to 1. A numerical example is considered to illustrate the computational approach. The overall impact of the uncertain factors on the optimization process is assessed by comparing the present numerical results with those given by the traditional approach.

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Four heuristic optimization algorithms applied to wind energy: determination of Weibull curve parameters for three Brazilian sites

International Journal of Energy and Environmental Engineering ◽

10.1007/s40095-018-0285-5 ◽

2018 ◽

Vol 10 (1) ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Carla Freitas de Andrade ◽

Lindemberg Ferreira dos Santos ◽

Marcus V. Silveira Macedo ◽

Paulo A. Costa Rocha ◽

Felipe Ferreira Gomes

Keyword(s):

Wind Energy ◽

Optimization Algorithms ◽

Heuristic Optimization ◽

Energy Determination

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α-Selective Glycosylation of 3,6-O-o-Xylylene-Bridged Glucosyl Fluoride

Synthesis ◽

10.1055/s-0036-1590927 ◽

2017 ◽

Vol 50 (02) ◽

pp. 282-294 ◽

Cited By ~ 6

Author(s):

Hidetoshi Yamada ◽

Atsushi Motoyama ◽

Tomoki Arai ◽

Kazutada Ikeuchi ◽

Kazuya Aki ◽

...

Keyword(s):

Three Dimensional ◽

Optimization Process ◽

Dimensional Structure ◽

Kinetic Control ◽

Pyranose Ring ◽

Reaction Conditions ◽

Three Dimensional Structure

A 1,2-cis-(α)-selective glycosylation has been developed. An ortho-xylylene group bridged between 3-O and 6-O of d-glucosyl fluoride, which straddles the β-face of the pyranose ring, hinders the approach of glycosyl acceptors from that face. The determination of the three-dimensional structure of the bridged glucosyl fluoride, the optimization process of the reaction conditions oriented toward kinetic control to realize the high α-selectivity, and the scope of the reaction are described.

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Determination of Optimal Machining Conditions: A Coupled Uncertainty Model

Journal of Manufacturing Science and Engineering ◽

10.1115/1.538898 ◽

1998 ◽

Vol 122 (1) ◽

pp. 206-214 ◽

Cited By ~ 3

Author(s):

S. S. Rao ◽

Li Chen

Keyword(s):

Optimization Process ◽

Objective Functions ◽

Machining Processes ◽

Numerical Examples ◽

Machining Conditions ◽

Uncertainty Model ◽

Empirical Relations ◽

Computational Aspects

The problem of selecting optimal machining conditions, where the formulation involves the use of empirical relations, is considered. Since both randomness and fuzziness are associated with empirical relations, a coupled uncertainty model is proposed for manipulating these uncertainties. Equations are derived to establish the interrelation between the two types of uncertainties present in the objective functions and constraints of the optimization process. This permits a systematic handling of fuzziness in terms of randomness that is usually associated with experiments. The computational aspects of the approach are illustrated by two numerical examples dealing with the optimization of machining processes. [S1087-1357(00)70501-6]

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Anthropic Principle Algorithm:A new Heuristic Optimization Meth

Journal of Architectural Environment & Structural Engineering Research ◽

10.30564/jaeser.v1i1.353 ◽

2018 ◽

Vol 1 (1) ◽

Author(s):

Elder Oroski ◽

Pês S. Beatriz ◽

Lopez H. Rafael ◽

Bauchspiess Adolfo

Keyword(s):

Genetic Algorithm ◽

System Identification ◽

Heuristic Algorithm ◽

Heuristic Algorithms ◽

Optimization Problems ◽

Anthropic Principle ◽

Optimization Process ◽

Heuristic Optimization ◽

The Universe ◽

Made In

Heuristic optimization is an appealing method for solving some en- gineering problems, in which gradient information may not be available, or yet, when the problem presents many minima points. Thus, the goal of this paper is to present a new heuristic algorithm based on the Anthropic Prin- ciple, the Anthropic Principle Algorithm (APA). This algorithm is based on the following idea: the universe developed itself in the exact way to allow the existence of all current things, including life. This idea is very similar to the convergence in an optimization process. Arguing about the merit of the An- thropic Principle is not among the goals of this paper. This principle is treated only as an inspiration for heuristic optimization algorithms. In the final of the paper, some applications of the APA are presented. Classical problems such as Rosenbrock function minimization, system identification examples and min- imization of some benchmark functions are also presented. In order to vali- date the APA’s functionality, a comparison between the APA and the classic heuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimiza- tion (PSO) is made. In this comparison, the APA presented better results in majority of tested cases, proving that it has a great potential for application in optimization problems.

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