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Comparison of Three Evolutionary Algorithms: GA, PSO, and DE
Kachitvichyanukul, Voratas Korean Institute of Industrial Engineers 2012 Industrial Engineeering & Management Systems Vol.11 No.3
This paper focuses on three very similar evolutionary algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). While GA is more suitable for discrete optimization, PSO and DE are more natural for continuous optimization. The paper first gives a brief introduction to the three EA techniques to highlight the common computational procedures. The general observations on the similarities and differences among the three algorithms based on computational steps are discussed, contrasting the basic performances of algorithms. Summary of relevant literatures is given on job shop, flexible job shop, vehicle routing, location-allocation, and multimode resource constrained project scheduling problems.
Comparison of Three Evolutionary Algorithms
Voratas Kachitvichyanukul 대한산업공학회 2012 Industrial Engineeering & Management Systems Vol.11 No.3
This paper focuses on three very similar evolutionary algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). While GA is more suitable for discrete optimization, PSO and DE are more natural for continuous optimization. The paper first gives a brief introduction to the three EA techniques to highlight the common computational procedures. The general observations on the similarities and differences among the three algorithms based on computational steps are discussed, contrasting the basic performances of algorithms. Summary of relevant literatures is given on job shop, flexible job shop, vehicle routing, location-allocation, and multimode resource constrained project scheduling problems.
Wisittipanich, Warisa,Kachitvichyanukul, Voratas Korean Institute of Industrial Engineers 2013 Industrial Engineeering & Management Systems Vol.12 No.2
In the past decades, several algorithms based on evolutionary approaches have been proposed for solving job shop scheduling problems (JSP), which is well-known as one of the most difficult combinatorial optimization problems. Most of them have concentrated on finding optimal solutions of a single objective, i.e., makespan, or total weighted tardiness. However, real-world scheduling problems generally involve multiple objectives which must be considered simultaneously. This paper proposes an efficient particle swarm optimization based approach to find a Pareto front for multi-objective JSP. The objective is to simultaneously minimize makespan and total tardiness of jobs. The proposed algorithm employs an Elite group to store the updated non-dominated solutions found by the whole swarm and utilizes those solutions as the guidance for particle movement. A single swarm with a mixture of four groups of particles with different movement strategies is adopted to search for Pareto solutions. The performance of the proposed method is evaluated on a set of benchmark problems and compared with the results from the existing algorithms. The experimental results demonstrate that the proposed algorithm is capable of providing a set of diverse and high-quality non-dominated solutions.
Differential Evolution Algorithm for Job Shop Scheduling Problem
Wisittipanich, Warisa,Kachitvichyanukul, Voratas Korean Institute of Industrial Engineers 2011 Industrial Engineeering & Management Systems Vol.10 No.3
Job shop scheduling is well-known as one of the hardest combinatorial optimization problems and has been demonstrated to be NP-hard problem. In the past decades, several researchers have devoted their effort to develop evolutionary algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for job shop scheduling problem. Differential Evolution (DE) algorithm is a more recent evolutionary algorithm which has been widely applied and shown its strength in many application areas. However, the applications of DE on scheduling problems are still limited. This paper proposes a one-stage differential evolution algorithm (1ST-DE) for job shop scheduling problem. The proposed algorithm employs random key representation and permutation of m-job repetition to generate active schedules. The performance of proposed method is evaluated on a set of benchmark problems and compared with results from an existing PSO algorithm. The numerical results demonstrated that the proposed algorithm is able to provide good solutions especially for the large size problems with relatively fast computing time.
Recent Advances in Adaptive Particle Swarm Optimization Algorithms
The Jin Ai,Voratas Kachitvichyanukul 대한산업공학회 2008 대한산업공학회 추계학술대회논문집 Vol.2008 No.11
This paper reviews recent literature on the mechanisms for adapting parameters of particle swarm optimization (PSO) algorithm. The review covers the mechanisms for adaptively setting such parameters as inertia weight, acceleration constants, number of particles and number of iterations. In additions, a more thorough review of the mechanisms for adapting inertia weight and acceleration constants is made. Detailed description of two specific mechanisms that are reviewed in detailed include the velocity index pattern for adapting the inertia weight and the relative gaps between various learning terms and the best objective function values for adapting the acceleration constants. An example to demonstrate the mechanisms are illustrated by using the GLNPSO for a specific optimization problem, namely, the vehicle routing problem. The preliminary experiment indicates that the addition of the proposed adaptive mechanisms can provide good algorithm performance in terms of solution quality with a slightly slower computational time.
A Tradeoff between Customer Efficiency and Firm Productivity in Service Delivery Systems
Trinh, Truong Hong,Kachitvichyanukul, Voratas,Luong, Huynh Trung Korean Institute of Industrial Engineers 2012 Industrial Engineeering & Management Systems Vol.11 No.3
The paper proposes a non-parametric methodology, data envelopment analysis, for measuring efficiency and productivity in service delivery systems with capacity constraints. The methodology provides allocation approaches for studying behaviors of firm and customers in service delivery strategy. The experimental study is carried out to investigate allocation behaviors and conduct an objective tradeoff between efficiency approach and productivity approach. The experimental result indicates that the efficiency approach allocates resource via maximizing customer efficiency rather than firm productivity as in the productivity approach. Moreover, the experiment reveals that there exists an objective tradeoff between the efficiency approach and the productivity approach. These findings provide strategic options for allocation policy in service delivery systems.
Differential Evolution Algorithm for Job Shop Scheduling Problem
Warisa Wisittipanich,Voratas Kachitvichyanukul 대한산업공학회 2011 Industrial Engineeering & Management Systems Vol.10 No.3
Job shop scheduling is well-known as one of the hardest combinatorial optimization problems and has been demonstrated to be NP-hard problem. In the past decades, several researchers have devoted their effort to develop evolutionary algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for job shop scheduling problem. Differential Evolution (DE) algorithm is a more recent evolutionary algorithm which has been widely applied and shown its strength in many application areas. However, the applications of DE on scheduling problems are still limited. This paper proposes a one-stage differential evolution algorithm (1ST-DE) for job shop scheduling problem. The proposed algorithm employs random key representation and permutation of m-job repetition to generate active schedules. The performance of proposed method is evaluated on a set of benchmark problems and compared with results from an existing PSO algorithm. The numerical results demonstrated that the proposed algorithm is able to provide good solutions especially for the large size problems with relatively fast computing time.