A multi-objective hybrid metaheuristic for zone definition procedure

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

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).

Self- assessed general health status is a commonly-used survey technique since it can be used as a predictor for several public health risks such as mortality, deprivation, and fear of crime or poverty. Therefore, it is a useful alternative measure to help assessing the public health situation of a neighborhood or town, and can be utilized by authorities in many decision support situations related to public health, budget allocation and general policy-making, among others. It can be considered as spatial decision problems, since both data location and spatial relationships make a prominent impact during the decision making process. This paper utilizes a recently-developed spatial intelligent decision system, named, Spatial RIMER(+), to model the self-rated health estimation decision problem using real data in the areas of Northern Ireland, UK. The goal is to learn from past or partial observations on self-rated health status to predict its future or neighborhood behavior and reference it in the map. Three scenarios in line of this goal are discussed in details, i.e., estimation of unknown, downscaling, and predictions over time. They are used to demonstrate the flexibility and applicability of the spatial decision support system and their positive capabilities in terms of accuracy, efficiency and visualization.

The planning of preparation and development activities in underground mining is essential to ensure efficiency and operational continuity. However, short-term scheduling of these tasks has received limited attention in literature. This study proposes a Cooperative Multi-start Tabu Search with Path-Relinking (CMTS-PR), which coordinates multiple tabu trajectories and intensifies them through path relinking to optimize the short-term scheduling of multiple underground heading works within a one-shift horizon. The problem is modeled as a flexible job-shop scheduling problem with unrelated parallel equipment and sequence dependent setup times. CMTS-PR is evaluated against a memetic algorithm, a Non-dominated Sorting Genetic Algorithm II, a single-trajectory Tabu Search (TS), a Constraint Programming (CP) model, and manual scheduling by an expert planner, across two panel caving case studies in Chile. The results show that CP yields mathematically optimal solutions but becomes computationally demanding, while manual scheduling ensures feasibility but underutilizes resources. In contrast, CMTS-PR produces operationally viable schedules. In case study 1, CMTS-PR matched CP on equivalent fronts within 60 seconds, even under 10-60 minute transfer time variability. In case study 2, CMTS-PR increased equivalent fronts by 120% compared to manual planning and by 2.94% relative to CP, with lower runtime. Overall, CMTS-PR proves to be effective and computationally efficient, representing one of the first applications of a cooperative TS and path-relinking scheme to underground short-term scheduling, and providing a practical tool for daily mine operations.

Some visual applications, such as virtual blackboards or gesture recognition for HCI applications, rely on precise visual tracking algorithms. In order to provide this accuracy, some refinement methods are needed as a complement to a coarser approach. In this case, Local Search (LS) methods are typically used, as their purpose is the exploration of a search space to improve a previous estimation. In this category fall many search algorithms such as Tabu Search, Simulated Annealing, Scatter Search, etc.

This paper proposes a new method for transmission network expansion planning (TNEP) with Multi-objective Memetic Algorithm (MOMA) in consideration of a probabilistic reliability index. Recently, power networks increase the degree of the uncertainties due to the new environment of power network liberations, the emergences of renewable energy, etc. As a result, the importance of improving power supply reliability with probabilistic approaches has been recognized in power system operation and planning. This paper formulates TNEP as a multi-objective optimization problem that optimizes a probabilistic reliability index as well as the construction cost to obtain a set of the Pareto solutions in Monte Carlo Simulation (MCS). This paper proposes a new method for TNEP with MOMA that combines Multi-objective meta-heuristics (MOMH) with Tabu Search (TS) to obtain better solution sets. MOMH is useful for evaluating a set of the Pareto solutions systematically while TS plays a key role to improve the solution quality. The effectiveness of the proposed method is successfully demonstrated in the IEEE 24-bus system.

A scatter search approach for the minimum sum-of-squares clustering problem

Meta-heuristic approaches with memory and evolution for a multi-product production/distribution system design problem

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.

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

In this paper, we propose an integrated genetic algorithm with hill climbing to solve the matrix bandwidth minimization problem, which is to reduce bandwidth by permuting rows and columns resulting in the nonzero elements residing in a band as close as possible to the diagonal. Many algorithms for this problem have been developed, including the well-known CM and GPS algorithms. Recently, Marti et al., (2001) used tabu search and Pinana et al. (2002) used GRASP with path relinking, separately, where both approaches outperformed the GPS algorithm. In this work, our approach is to exploit the genetic algorithm technique in global search while using hill climbing for local search. Experiments show that this approach achieves the best solution quality when compared with the GPS algorithm, tabu search, and the GRASP with path relinking methods, while being faster than the latter two newly-developed heuristics.

We propose a node centroid method with Hill-Climbing to solve the well-known matrix bandwidth minimization problem, which is to permute rows and columns of the matrix to minimize its bandwidth. Many heuristics have been developed for this NP-complete problem including the Cuthill-McKee (CM) and the Gibbs, Poole and Stockmeyer (GPS) algorithms. Heuristics such as simulated annealing, tabu search and GRASP have been used, where tabu search and the GRASP with path relinking have achieved significantly better solution quality than the CM and GPS algorithms. Experimentation shows that the node centroid method achieves the best solution quality when compared with these while being much faster than the newly-developed algorithms. Also, the new algorithm achieves better solutions than the GPS algorithm in comparable time.