Solving the tool switching problem with memetic algorithms

2011 ◽  
Vol 26 (2) ◽  
pp. 221-235 ◽  
Author(s):  
Jhon Edgar Amaya ◽  
Carlos Cotta ◽  
Antonio J. Fernández-Leiva
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Ad Hoc ◽  
Memetic Algorithm ◽  
Memetic Algorithms ◽  
Hill Climbing ◽  
Beam Search ◽  
Steepest Ascent ◽  
Tool Switching ◽  
Better Than

AbstractThe tool switching problem (ToSP) is well known in the domain of flexible manufacturing systems. Given a reconfigurable machine, the ToSP amounts to scheduling a collection of jobs on this machine (each of them requiring a different set of tools to be completed), as well as the tools to be loaded/unloaded at each step to process these jobs, such that the total number of tool switches is minimized. Different exact and heuristic methods have been defined to deal with this problem. In this work, we focus on memetic approaches to this problem. To this end, we have considered a number of variants of three different local search techniques (hill climbing, tabu search, and simulated annealing), and embedded them in a permutational evolutionary algorithm. It is shown that the memetic algorithm endowed with steepest ascent hill climbing search yields the best results, performing synergistically better than its stand-alone constituents, and providing better results than the rest of the algorithms (including those returned by an effective ad hoc beam search heuristic defined in the literature for this problem).

Analyzing self-★ island-based memetic algorithms in heterogeneous unstable environments

2016 ◽  
Vol 32 (5) ◽  
pp. 676-692 ◽  
Author(s):  
Rafael Nogueras ◽  
Carlos Cotta
Keyword(s):  
Memetic Algorithm ◽  
Memetic Algorithms ◽  
Population Based ◽  
Self Healing ◽  
Computational Power ◽  
Ability To Work ◽  
Computing Power ◽  
Simulated Environment ◽  
Computational Resources ◽  
Better Than

Computational environments emerging from the pervasiveness of networked devices offer a plethora of opportunities and challenges. The latter arise from their dynamic, inherently volatile nature that tests the resilience of algorithms running on them. Here we consider the deployment of population-based optimization algorithms on such environments, using the island model of memetic algorithms for this purpose. These memetic algorithms are endowed with self-★ properties that give them the ability to work autonomously in order to optimize their performance and to react to the instability of computational resources. The main focus of this work is analyzing the performance of these memetic algorithms when the underlying computational substrate is not only volatile but also heterogeneous in terms of the computational power of each of its constituent nodes. To this end, we use a simulated environment that allows experimenting with different volatility rates and heterogeneity scenarios (that is, different distributions of computational power among computing nodes), and we study different strategies for distributing the search among nodes. We observe that the addition of self-scaling and self-healing properties makes the memetic algorithm very robust to both system instability and computational heterogeneity. Additionally, a strategy based on distributing single islands on each computational node is shown to perform globally better than placing many such islands on each of them (either proportionally to their computing power or subject to an intermediate compromise).

Fitness Landscapes, Memetic Algorithms, and Greedy Operators for Graph Bipartitioning

2000 ◽  
Vol 8 (1) ◽  
pp. 61-91 ◽  
Author(s):  
Peter Merz ◽  
Bernd Freisleben
Keyword(s):  
Local Search ◽  
Fitness Landscape ◽  
Memetic Algorithm ◽  
Search Space ◽  
Memetic Algorithms ◽  
Dependency Graph ◽  
Gene Interactions ◽  
Local Minima ◽  
Problem Size ◽  
Better Than

The fitness landscape of the graph bipartitioning problem is investigated by performing a search space analysis for several types of graphs. The analysis shows that the structure of the search space is significantly different for the types of instances studied. Moreover, with increasing epistasis, the amount of gene interactions in the representation of a solution in an evolutionary algorithm, the number of local minima for one type of instance decreases and, thus, the search becomes easier. We suggest that other characteristics besides high epistasis might have greater influence on the hardness of a problem. To understand these characteristics, the notion of a dependency graph describing gene interactions is introduced. In particular, the local structure and the regularity of the dependency graph seems to be important for the performance of an algorithm, and in fact, algorithms that exploit these properties perform significantly better than others which do not. It will be shown that a simple hybrid multi-start local search exploiting locality in the structure of the graphs is able to find optimum or near optimum solutions very quickly. However, if the problem size increases or the graphs become unstructured, a memetic algorithm (a genetic algorithm incorporating local search) is shown to be much more effective.

scholarly journals One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3030
Author(s):  
Raúl Mencía ◽  
Carlos Mencía
Keyword(s):  
Local Search ◽  
Electric Vehicles ◽  
Memetic Algorithm ◽  
Memetic Algorithms ◽  
Hill Climbing ◽  
Search Methods ◽  
Local Search Methods ◽  
Local Search Procedure ◽  
One Machine Scheduling ◽  
One Machine

This paper addresses the problem of scheduling a set of jobs on a machine with time-varying capacity, with the goal of minimizing the total tardiness objective function. This problem arose in the context scheduling the charging times of a fleet of electric vehicles and it is NP-hard. Recent work proposed an efficient memetic algorithm for solving the problem, combining a genetic algorithm and a local search method. The local search procedure is based on swapping consecutive jobs on a C-path, defined as a sequence of consecutive jobs in a schedule. Building on it, this paper develops new memetic algorithms that stem from new local search procedures also proposed in this paper. The local search methods integrate several mechanisms to make them more effective, including a new condition for swapping pairs of jobs, a hill climbing approach, a procedure that operates on several C-paths and a method that interchanges jobs between different C-paths. As a result, the new local search methods enable the memetic algorithms to reach higher-quality solutions. Experimental results show significant improvements over existing approaches.

Real-Coded Memetic Algorithms with Crossover Hill-Climbing

2004 ◽  
Vol 12 (3) ◽  
pp. 273-302 ◽  
Author(s):  
Manuel Lozano ◽  
Francisco Herrera ◽  
Natalio Krasnogor ◽  
Daniel Molina
Keyword(s):  
Local Search ◽  
Memetic Algorithm ◽  
Memetic Algorithms ◽  
Population Diversity ◽  
Hill Climbing ◽  
Problem Instance ◽  
Genetic Operators ◽  
Local Tuning ◽  
Wide Range ◽  
The One

This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the self-adaptive capacity of real-parameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities to individuals. It was observed that the algorithm adjusts the global/local search balance according to the particularities of each problem instance. Experimental results show that, for a wide range of problems, the method we propose here consistently outperforms other real-coded memetic algorithms which appeared in the literature.

Optimization of Linear Layout Problem in Flexible Manufacturing System Using Evolutionary Algorithms - Case Study

2015 ◽  
Vol 766-767 ◽  
pp. 896-901
Author(s):  
M. Saravanan ◽  
S. Ganesh Kumar ◽  
V. Srinivasa Raman
Keyword(s):  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Flexible Manufacturing System ◽  
Transportation Cost ◽  
Sheep Flock ◽  
Layout Problem ◽  
Proposed Model ◽  
Better Than

Linear layout is the commonly major preferred arrangement of the flexible manufacturing systems (FMS). The proposed enhanced sheep flock heredity algorithm to solving the unequal area linear layout problem through the real case study problem. The proposed model is to minimize the transportation cost with non-overlapping. Computational results show that proposed sheep flock heredity algorithm (SFHA) can obtain better than particle swarm optimization (PSO) and existing method.

scholarly journals Enhancement of Computational Efficiency in Seeking Liveness-Enforcing Supervisors for Advanced Flexible Manufacturing Systems with Deadlock States

2020 ◽  
Vol 10 (7) ◽  
pp. 2620
Author(s):  
Yen-Liang Pan ◽  
Chun-Wang Tai ◽  
Ching-Yun Tseng ◽  
Jong-Ching Huang
Keyword(s):  
Computational Efficiency ◽  
Flexible Manufacturing ◽  
Flexible Manufacturing Systems ◽  
Manufacturing Systems ◽  
Computational Cost ◽  
Production Capacity ◽  
New Policies ◽  
Resources Sharing ◽  
High Degree ◽  
Better Than

In industry 4.0, all kinds of intelligent workstations are designed for use in manufacturing industries. Among them, flexible manufacturing systems (FMSs) use smart robots to achieve their production capacity under the condition of a high degree of resources sharing. As a result, deadlock states usually appear unexpectedly. For solving the damage deadlock problem, many pioneers have proposed new policies. However, it is very difficult to make systems maximally permissive even if their policies can solve the deadlock problem of FMSs. According to our survey, the Maximal number of Forbidding First Bad Marking (FBM) Problems (MFFP) seems to be the best technology to obtain systems’ maximally permissive states in the existing literature. More importantly, the number of added control places (CP) is the smallest among the existing research works. However, when the complexity of a flexible manufacturing system increases, the computational burden rises rapidly. To reduce computational cost, we define a new concept named Pre Idle Places (PIP) to enhance the computational efficiency in Seeking Liveness-Enforcing Supervisors. We can bypass all PIP once they can be identified from a deadlock system under the process of solving MFFP. According to the data showed in three classical examples, our proposed Improved MFFP is better than conventional MFFP in terms of computational efficiency with the same controllers.

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