Multi-objective optimization with improved genetic algorithm

Ishibashi, Hiroyuki; Aguirre, Hernán; Tanaka, Kiyoshi; Sugimura, Tatsuo

doi:10.1109/icsmc.2000.886611

Cited by 16 publications

(13 citation statements)

References 10 publications

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“…Mahadvi et al(2008) also used weighted sum to tackle the s-stage flow shop with serial batch production at the last stage, where multiple objectives are addressed for minimizing the total weighted earliness, the total weighted tardiness and the total weighted waiting time. Other related finding can be found from (Ruiz and Allahverdi, 2009, Ishibashi et al, 2000, Yandra and Tamura, 2007.…”

Section: Literature Reviewmentioning

confidence: 77%

Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs

Lee

Lin

et al. 2011

Int J Adv Manuf Technol

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This paper presents a simulated genetic algorithm (GA) model of scheduling the flow shop problems with re-entrant jobs. The objectives of this research are to minimize the weighted tardiness and makespan. The proposed model considers that the jobs with non-identical due dates are processed on the machines in the same order. Furthermore, the re-entrant jobs are stochastic as only some jobs are required to reenter to the flow shop. The tardiness weight is adjusted once the jobs re-enter to the shop. The performance of the proposed GA model is verified by a number of numerical experiments where the data come from the case company. The results show the proposed method has a higher order satisfaction rate than the current industrial practices.

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Section: Literature Reviewmentioning

confidence: 77%

Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs

Lee

Lin

et al. 2011

Int J Adv Manuf Technol

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“…Also, we are planning to continue studying moGA-SRM's behavior in a wider range of problems that include more than two objectives [18] and use it in other real world applications.…”

Section: Discussionmentioning

confidence: 98%

“…The parallel formulation of genetic operators tied to extinctive selection creates a cooperative-competitive environment for the offspring created by CM and SRM. GA-SRM based on this model remarkably improves the search performance of GA [10,14,18].…”

Section: Concept Of Ga-srmmentioning

confidence: 92%

Halftone Image Generation with Improved Multiobjective Genetic Algorithm

Aguirre

Tanaka

Sugimura

et al. 2001

Lecture Notes in Computer Science

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Abstract.A halftoning technique that uses a simple GA has proven to be very effective to generate high quality halftone images. Recently, the two major drawbacks of this conventional halftoning technique with GAs, i.e. it uses a substantial amount of computer memory and processing time, have been overcome by using an improved GA (GA-SRM) that applies genetic operators in parallel putting them in a cooperative-competitive stand with each other. The halftoning problem is a true multiobjective optimization problem. However, so far, the GA based halftoning techniques have treated the problem as a single objective optimization problem. In this work, the improved GA-SRM is extended to a multiobjective optimization GA to generate simultaneously halftone images with various combinations of gray level and spatial resolution. Simulation results verify that the proposed scheme can effectively generate several high quality images simultaneously in a single run reducing even further the overall processing time.

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“…The crossover parents share the edge-lists on which several manipulations are repeated until all edge-lists are processed. Ishibashi et al [50] proposed a two-point ordered crossover that randomly selects two crossing points from parents and decides which segment should be inherited to the offspring.…”

Section: Route-exchange Crossovermentioning

confidence: 99%

A Hybrid Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Time Windows

Tan

Chew

Lee

2005

Comput Optim Applic

211

110

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Vehicle routing problem with time windows (VRPTW) involves the routing of a set of vehicles with limited capacity from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. The problem is solved by optimizing routes for the vehicles so as to meet all given constraints as well as to minimize the objectives of traveling distance and number of vehicles. This paper proposes a hybrid multiobjective evolutionary algorithm (HMOEA) that incorporates various heuristics for local exploitation in the evolutionary search and the concept of Pareto's optimality for solving multiobjective optimization in VRPTW. The proposed HMOEA is featured with specialized genetic operators and variablelength chromosome representation to accommodate the sequence-oriented optimization in VRPTW. Unlike existing VRPTW approaches that often aggregate multiple criteria and constraints into a compromise function, the proposed HMOEA optimizes all routing constraints and objectives simultaneously, which improves the routing solutions in many aspects, such as lower routing cost, wider scattering area and better convergence trace. The HMOEA is applied to solve the benchmark Solomon's 56 VRPTW 100-customer instances, which yields 20 routing solutions better than or competitive as compared to the best solutions published in literature.

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Multi-objective optimization with improved genetic algorithm

Cited by 16 publications

References 10 publications

Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs

Design of a genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs

Halftone Image Generation with Improved Multiobjective Genetic Algorithm

A Hybrid Multiobjective Evolutionary Algorithm for Solving Vehicle Routing Problem with Time Windows

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