GISMOO: A new hybrid genetic/immune strategy for multiple-objective optimization

Zinflou, Arnaud; Gagné, Caroline; Gravel, Marc

doi:10.1016/j.cor.2011.08.020

Cited by 21 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It aims to support research that satisfies all the possible objectives [26][27][28]. Zinflou proposed, in [29], a new Pareto generic algorithm, called GISMOO, which hybridizes genetic algorithm and artificial immune systems. Several recent multi-objective algorithms were inspired either by Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) or by Strength Pareto Evolutionary Algorithm 2 (SPEA2).…”

Section: Multi-criteria Optimization Problemmentioning

confidence: 99%

Genetic Algorithm for Multi-Criteria Optimization of Multi-Depots Pick-up and Delivery Problems with Time Windows and Multi-Vehicles

Alaïa¹,

Dridi

Bouchriha³

et al. 2015

APH

View full text Add to dashboard Cite

In this paper, multi-vehicles, multi-depots pick-up and delivery problems with time windows (m-MDPDPTW) is presented as a multi-criteria optimization problem. The m-MDPDPTW is a variant of pick-up and delivery problem (PDP) and a challenging problem in the field of vehicle routing problem (VRP). The aim is to discover a set of satisfying solutions (routes) minimizing total travel distance, total tardiness time and the total number of vehicles. These routes satisfy transportation requests without contravening any of the instance specific constraints (precedence, capacity and time window constraints). In our problem each request is transported by one of the vehicles between paired pick-up and delivery locations. Such that, the depot does not retain the goods. In this paper, we assume that all vehicles have the same capacity and depart from and return to the same depot. The new encoding and structure algorithm on which this contribution is based uses a genetic algorithm, a selection process using ranking with several Pareto fronts and an elitist selection strategy for replacement. An improved encoding chromosome path representation is given to simulate the process of evolution using several types of populations in different sizes. The performance of the new algorithm is tested on data sets instances of Li & Lim's PDPTW benchmark problems. The results indicate that the proposed algorithm gives good results.

show abstract

Section: Multi-criteria Optimization Problemmentioning

confidence: 99%

Genetic Algorithm for Multi-Criteria Optimization of Multi-Depots Pick-up and Delivery Problems with Time Windows and Multi-Vehicles

Alaïa¹,

Dridi

Bouchriha³

et al. 2015

APH

View full text Add to dashboard Cite

show abstract

“…Among various multiobjective optimization algorithms, multiobjective evolutionary algorithms (MOEA), which make use of the strategy of the population evolutionary to optimize the problems, are an effective method for solving MOPs. In recent years, many MOEAs have been proposed for solving the multiobjective optimization problems [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. In the MOEA literatures, Goldberg's population categorization strategy [19] based on nondominance is important.…”

Section: Introductionmentioning

confidence: 99%

A New Multiobjective Evolutionary Algorithm Based on Decomposition of the Objective Space for Multiobjective Optimization

Dai

Wang

2014

Journal of Applied Mathematics

View full text Add to dashboard Cite

In order to well maintain the diversity of obtained solutions, a new multiobjective evolutionary algorithm based on decomposition of the objective space for multiobjective optimization problems (MOPs) is designed. In order to achieve the goal, the objective space of a MOP is decomposed into a set of subobjective spaces by a set of direction vectors. In the evolutionary process, each subobjective space has a solution, even if it is not a Pareto optimal solution. In such a way, the diversity of obtained solutions can be maintained, which is critical for solving some MOPs. In addition, if a solution is dominated by other solutions, the solution can generate more new solutions than those solutions, which makes the solution of each subobjective space converge to the optimal solutions as far as possible. Experimental studies have been conducted to compare this proposed algorithm with classic MOEA/D and NSGAII. Simulation results on six multiobjective benchmark functions show that the proposed algorithm is able to obtain better diversity and more evenly distributed Pareto front than the other two algorithms.

show abstract

“…Optimization problems often involve the need to solve several objectives; therefore, many studies focus on the processing method and algorithm of multi-objective optimization problems [27]. Because the supply-chain design often involves multiple parts, multiple subjects and multiple perspectives, multiple-objective optimization is commonly adopted in green supply-chain design [28][29][30].…”

Section: Model Constructionmentioning

confidence: 99%

“…Taking Q 1 as an example, construct the random expectation planning as follows: ( 26) s.t. q 1`q2`q3`q4 ď Q 1 γ 1 (27) (1) Construct the uncertainty function as follows: …”

Section: Model Solutionmentioning

confidence: 99%

The Optimization of Cyclic Links of Live Pig-Industry Chain Based on Circular Economics

Liu

2015

Sustainability

View full text Add to dashboard Cite

Abstract:To reduce waste and wastewater pollution and to improve the utilization rate of resources in the pig-industry chain, a circular economy of the chain can be developed. The key to constructing the circular economic system of the pig-industry chain is to determine the path of the cyclic materials and to design reasonable waste-and wastewater-treatment capacities. This paper focuses on the treatment and recycling of wastewater in the pig-industry chain and the treatment and recycling of waste into manure and feed. After giving the two circular paths, the paper proposes a multi-objective uncertainty-optimization model for the cyclic links of the pig-industry chain with the highest resource-reuse efficiency and the lowest construction cost based on the uncertainty of market demand. Using a combination of the neural network and genetic algorithm method for designing the solution process for the model, the paper finally introduces the determination methods of relevant parameters and verifies the feasibility and effectiveness of the model through a case study.

show abstract

GISMOO: A new hybrid genetic/immune strategy for multiple-objective optimization

Cited by 21 publications

References 19 publications

Genetic Algorithm for Multi-Criteria Optimization of Multi-Depots Pick-up and Delivery Problems with Time Windows and Multi-Vehicles

Genetic Algorithm for Multi-Criteria Optimization of Multi-Depots Pick-up and Delivery Problems with Time Windows and Multi-Vehicles

A New Multiobjective Evolutionary Algorithm Based on Decomposition of the Objective Space for Multiobjective Optimization

The Optimization of Cyclic Links of Live Pig-Industry Chain Based on Circular Economics

Contact Info

Product

Resources

About