Hybrid scatter search and path relinking for the capacitated p-median problem

In this study, the performance of Scatter Search (SS) meta-heuristic for the post-enrolment course timetabling problems reported in recent literature was reviewed. The aim is to address the strengths and limitations of the SS structure and mechanisms; to empower more studies; to investigate the capabilities of SS; and to enhance it for solving timetabling problems as a whole. The SS is almost similar to memetic algorithms. However, it has a memory of elite solutions (which considers both high quality and diverse solutions) and combines two or more solutions explicitly based on elitism. SS contains a mechanism to strike a balance between diversification and intensification of the search. SS has five major steps: initializing a diverse collection of solutions; general solutions improvement; memorizing elite solutions, solutions combination; and improving the selected solution. Based on the outcomes of SS (tested on post-enrolment course timetabling problems), which was reported in the literature, this study concluded that the impact of SS's strategies are significant for a successful SS search performance. These strategies are: memory update, solutions selection, diversification (i.e. similarity measurement), and solutions combination. Indeed, updating the memory and solutions combination, have the greatest impact on the performance of SS. Therefore, future studies regarding the design of these two strategies for solving timetabling problems is recommended to be more carefully designed.

Chapter 2: An Introduction to Genetic Algorithms for Engineering Applications Chapter 3: Memetic Algorithms Chapter 4: Scatter Search and Path Relinking: Foundations and Advanced Designs Chapter 5: Ant Colony Optimization Chapter 6: Differential Evolution Chapter 7: SOMA-Self-Organizing Migrating Algorithm Chapter 8: Discrete Particle Swarm Optimization:Illustrated by the Traveling Salesman Problem

A statistical analysis in the BWR fuel lattice design

Single machine scheduling problems have many real-life applications and may be hard to solve due to the particular characteristics of some production environments. In this paper, we tackle the single machine scheduling problem with sequence-dependent setup times with the objective of minimizing the weighted tardiness. To solve this problem, we propose a scatter search algorithm which uses path relinking in its core. This algorithm is enhanced with some procedures to speed-up the neighbors' evaluation and with some diversification and intensification techniques, the latter taking some elements from iterated local search. We conducted an experimental study across a well-known set of instances to analyze the contribution of each component to the overall performance of the algorithm, as well as to compare our proposal with the state-of-the-art metaheuristics, obtaining competitive results. We also propose a new benchmark with larger and more challenging instances and provide the first results for them.

Scatter search (SS) and path relinking (PR) are evolutionary methods that have been successfully applied to a wide range of hard optimization problems. The fundamental concepts and principles of the methods were first proposed in the 1970s and 1980s, and were based on formulations, dating back to the 1960s, for combining decision rules and problem constraints. The methods use strategies for search diversification and intensification that have proved effective in a variety of optimization problems and that have sometimes been embedded in other evolutionary methods to yield improved performance. This paper examines the scatter search and path relinking methodologies from both conceptual and practical points of view, and identifies certain connections between their strategies and those adopted more recently by particle swarm optimization. The authors describe key elements of the SS & PR approaches and apply them to a hard combinatorial optimization problem: the minimum linear arrangement problem, which has been used in applications of structural engineering, VLSI and software testing.

Zone Definition Procedure (ZDP) is defined as the drawing of territory lines for geographical zones for space control. It is a spatial multiple criteria decision problem but there is limited research attempts in this aspect. Therefore, this paper presents a multi-objective hybrid metaheuristic for ZDP based on multi-objective definition. It is a more realistic solution to the real-world ZDP problem because it helps to consider the relationship among objectives with dominancy comparison among different objectives. The fusion of Tabu Search (TS), Scatter Search (SS) and Path Relinking (PR) was used in the ZDP search process. The exploration of the solution space is based on the strategic oscillation philosophy. An evaluation on the proposed one is done with a commonly used single objective hybrid metaheuristic. This study has conducted several testing and experiments to compare the quality of the results and computation effectiveness of the two approaches.

This section provides a collection of “vignettes” that briefly summarize applications of scatter search (SS) and path relinking (PR) in a variety of settings.

A recent study (Yin et al., 2010) showed that combining particle swarm optimization (PSO) with the strategies of scatter search (SS) and path relinking (PR) produces a Cyber Swarm Algorithm that creates a more effective form of PSO than methods that do not incorporate such mechanisms. This paper proposes a Complementary Cyber Swarm Algorithm (C/CyberSA) that performs in the same league as the original Cyber Swarm Algorithm but adopts different sets of ideas from the tabu search (TS) and the SS/PR template. The C/CyberSA exploits the guidance information and restriction information produced in the history of swarm search and the manipulation of adaptive memory. Responsive strategies using long term memory and path relinking implementations are proposed that make use of critical events encountered in the search. Experimental results with a large set of challenging test functions show that the C/CyberSA outperforms two recently proposed swarm-based methods by finding more optimal solutions while simultaneously using a smaller number of function evaluations. The C/CyberSA approach further produces improvements comparable to those obtained by the original CyberSA in relation to the Standard PSO 2007 method (Clerc, 2008).

The Scatter Search is one of the population-based metaheuristics, which can be used to solve a wide range of problems including Traveling Salesman Problem (TSP), Routing Protocol, and End-to-End QoS, while Bees Algorithm can be applied to optimize the solution of these problems. In this paper, an improved Scatter Search algorithm based on Bees Algorithm has been presented. The improvement provides Scatter Search with random exploration for search space of problem and more of intensification for promising solutions. The original and improved Scatter Search has been tested on Traveling Salesman Problem. A computational experiment with benchmark instances is reported. The results demonstrate that the improved Scatter Search algorithm produces better performance than original Scatter Search algorithm. The developed algorithm has been compared with other algorithms for the same problem, and the result was competitive with some algorithm and insufficient with another.

Scatter search is a population-based method that has recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints, such as the surrogate constraint method, scatter search uses strategies for combining solution vectors that have proved effective in a variety of problem settings. We present a scatter-search implementation designed to find high-quality solutions for the 3D image-registration problem, which has many practical applications. This problem arises in computer vision applications when finding a correspondence or transformation between two computer images obtained under different conditions. Our implementation goes beyond a simple exercise on applying scatter search, by incorporating innovative mechanisms to combine and improve solutions and to create a balance between intensification and diversification in the reference set. Furthermore, heuristic information taken from a preprocessing of the images is incorporated into the algorithm to improve its performance. Our computational experimentation tackling two different medical registration applications establishes the effectiveness of scatter search in relation to different approaches usually applied to solving the problem. We have considered both simulated magnetic resonance images and real-world computerized tomography images as data sets. To measure the robustness of our proposal, the image data sets are intentionally selected for addressing registration environments with the presence of noise, anatomical lesions, and occlusions between images.

The effect of elite pool in hybrid population-based meta-heuristics for solving combinatorial optimization problems

Truck scheduling is an important optimization problem for successful crossdocking operations. Current efforts less focus on the problem considering the constraints of product unloading and loading sequences in trucks, which makes scheduling optimization more difficult and easily aggravates inefficiency of unsuitable scheduling. This paper proposes an integer programming model for such problem in a multi-door crossdocking system. The objective is to find an optimal door assignment and sequencing of inbound and outbound trucks that minimizes the makespan and satisfies the constraints. For solving it, a scatter search (SS) algorithm is developed. Computational experiments are conducted to evaluate the performance of the proposed SS approach in comparison with genetic algorithm (GA). The computational results show that SS produces better solutions in shorter CPU times for problem instances with different scales.

An empirical investigation on parallelization strategies for Scatter Search

Optimization problems are very complex to be solved, as they can present multiple optimal solutions and, in certain cases, only a globally optimal solution. The mathematical formulation of these problems is composed of continuous and discrete variables with linear and nonlinear functions. The solution of these problems by mathematical modeling through solvers may not converge to a feasible solution or may demand much computational time, when many variables are considered. Thus, using metaheuristics to solve these problems is desirable according to the literature. The scatter search (SS) metaheuristic algorithm has been highlighted in the literature as an excellent combinatorial optimization algorithm for dealing with quality and diversity solutions. This work aims to present two methods using the SS algorithm to solve problems with continuous and discrete variables. Therefore, the SS algorithm is implemented to solve the traveling salesperson problem (TSP), which is a problem that contains only discrete variables, and also, another version of SS is implemented to solve the optimal power flow (OPF) problem, which has continuous and discrete variables. Two instances of the TSP and three instances of the OPF problem were used. The results show that the SS solves both problems efficiently and presents better results in some cases than the solutions of the same problems using classical mathematical models through solvers. It is concluded that implementing the SS algorithm does not require much effort and that good results can be achieved for solving optimization problems with variables of any nature.

A todos tengo que agradecer las sugerencias y/o preguntas sobre SSm que me han ayudado a encontrar errores, a mejorarlo y a aprender un poco ms. Quiero resaltar a algunos por su relacin ms