Modified genetic algorithm applied to solve product family optimization problem

2007 ◽  
Vol 20 (04) ◽  
pp. 106 ◽  
Author(s):  
Chunbao CHEN
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Product Family ◽  
Modified Genetic Algorithm ◽  
Product Family Optimization

scholarly journals Truss’ topology and shape optimization by genetic algorithms

2021 ◽  
Vol 47 ◽  
Author(s):  
Dmitrij Šešok ◽  
Paulius Ragauskas
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Shape Optimization ◽  
Optimization Problem ◽  
Numerical Solutions ◽  
Search Algorithm ◽  
Truss Topology ◽  
Modified Genetic Algorithm ◽  
Topology And Shape Optimization ◽  
System Characteristics

In the paper the global optimization problem of truss systems is studied.  The genetic algorithms are employed for the optimization. As the objective function the structure mass is treated; the constraints include equilibrium, local stability and other requirements.  All the truss system characteristics needed for genetic algorithm are obtained via finite element solution. Topology optimization of truss system is performed using original modified genetic algorithm, while the shape optimization – using ordinary genetic algorithm. Numerical solutions are presented. The obtained solutions are compared with global extremes obtained using full search algorithm.  All the numerical examples are solved using original software.

Multidisciplinary design optimization of vehicle chassis product family

2021 ◽  
pp. 095440542110366
Author(s):  
Xiaokai Chen ◽  
Chenyu Wang ◽  
Guobiao Shi ◽  
Mingkai Zeng
Keyword(s):  
Design Optimization ◽  
Design Process ◽  
Multidisciplinary Design Optimization ◽  
Optimization Design ◽  
Optimization Problem ◽  
Product Family ◽  
Multidisciplinary Design ◽  
Product Family Design ◽  
Product Platforms ◽  
Product Family Optimization

In order to improve the performance of automotive product platforms and product families while keeping high development efficiency, a product family optimization design method that combines shared variable decision-making and multidisciplinary design optimization (MDO) is proposed. First, the basic concepts related to product family design optimization were clarified. Then, the mathematical description and MDO model of the product family optimization problem were established, and the improved product family design process was given. Finally, for the chassis product family optimization problem of an automotive product platform, the effectiveness of the proposed optimization method, and design process were exemplified. The results show that the collaboratively optimized product family can effectively handle the coordination between multiple products and multiple targets, compared to Non-platform development, it can maximize the generalization rate of vehicle parts and components under the premise of ensuring key performance, and give full play to the advantages of product platforms.

Mission Design of a Team of Service Robots

2015 ◽  
pp. 728-749
Author(s):  
Elias K. Xidias ◽  
Nikos A. Aspragathos ◽  
Philip N. Azariadis
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Integrated Approach ◽  
Mission Design ◽  
Service Robots ◽  
Indoor Environments ◽  
Mathematical Entity ◽  
Modified Genetic Algorithm ◽  
Service Stations

The purpose of this chapter is to present an integrated approach for Mission Design of a team of Service Robots that is operating in partially known indoor environments such as libraries, hospitals, or warehouses. The robots are requested to serve a number of service stations while taking into account movement safety and other kinematical constraints. The Bump-Surface concept is used to represent the robots' environment through a single mathematical entity and an optimization problem is formulated representing an aggregation of paths length and movement constraints. Then a modified Genetic Algorithm with parallel populations is used for solving the problem of mission design of a team of service robots on the constructed Bump-Surface. Three simulation examples are presented to show the effectiveness of the presented approach.

scholarly journals The Multiagent Planning Problem

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Tamás Kalmár-Nagy ◽  
Giovanni Giardini ◽  
Bendegúz Dezső Bak
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Problem Solver ◽  
Planning Problem ◽  
Genetic Operators ◽  
Total Distance ◽  
Modified Genetic Algorithm ◽  
Multiagent Planning ◽  
Simple Paths ◽  
Better Than

The classical Multiple Traveling Salesmen Problem is a well-studied optimization problem. Given a set ofngoals/targets andmagents, the objective is to findmround trips, such that each target is visited only once and by only one agent, and the total distance of these round trips is minimal. In this paper we describe the Multiagent Planning Problem, a variant of the classical Multiple Traveling Salesmen Problem: given a set ofngoals/targets and a team ofmagents,msubtours (simple paths) are sought such that each target is visited only once and by only one agent. We optimize for minimum time rather than minimum total distance; therefore the objective is to find the Team Plan in which the longest subtour is as short as possible (a min–max problem). We propose an easy to implement Genetic Algorithm Inspired Descent (GAID) method which evolves a set of subtours using genetic operators. We benchmarked GAID against other evolutionary algorithms and heuristics. GAID outperformed the Ant Colony Optimization and the Modified Genetic Algorithm. Even though the heuristics specifically developed for Multiple Traveling Salesmen Problem (e.g.,k-split, bisection) outperformed GAID, these methods cannot solve the Multiagent Planning Problem. GAID proved to be much better than an open-source Matlab Multiple Traveling Salesmen Problem solver.

A Single-Stage Gradient-Based Approach for Solving the Joint Product Family Platform Selection and Design Problem Using Decomposition

2007 ◽  
Author(s):  
Aida Khajavirad ◽  
Jeremy J. Michalek
Keyword(s):  
Design Problem ◽  
Optimization Problem ◽  
Computational Cost ◽  
Product Family ◽  
Single Stage ◽  
System Level ◽  
Continuous Space ◽  
Joint Product ◽  
Product Family Optimization ◽  
Gradient Based

A core challenge in product family optimization is to develop a single-stage approach that can optimally select the set of variables to be shared in the platform(s) while simultaneously designing the platform(s) and variants within an algorithm that is efficient and scalable. However, solving the joint product family platform selection and design problem involves significant complexity and computational cost, so most prior methods have narrowed the scope by treating the platform as fixed or have relied on stochastic algorithms or heuristic two-stage approaches that may sacrifice optimality. In this paper, we propose a single-stage approach for optimizing the joint problem using gradient-based methods. The combinatorial platform-selection variables are relaxed to the continuous space by applying the commonality index and consistency relaxation function introduced in a companion paper. In order to improve scalability properties, we exploit the structure of the product family problem and decompose the joint product family optimization problem into a two-level optimization problem using analytical target cascading so that the system-level problem determines the optimal platform configuration while each subsystem optimizes a single product in the family. Finally, we demonstrate the approach through optimization of a family of ten bathroom scales; Results indicate encouraging success with scalability and computational expense.

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