Showing papers on "Local search (optimization) published in 2009"
21 Apr 2009
TL;DR: Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species as discussed by the authors.
Abstract: Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species [1]. Artificial ants in ACO essentially are randomized construction procedures that generate solutions based on (artificial) pheromone trails and heuristic information that are associated to solution components. Since the first ACO algorithm has been proposed in 1991, this algorithmic method has attracted a large number of researchers and in the meantime it has reached a significant level of maturity. In fact, ACO is now a well-established search technique for tackling a wide variety of computationally hard problems.
2,424 citations
TL;DR: The computational results have proved that the proposed hybrid algorithm is an efficient and effective approach to solve the multi-objective FJSP, especially for the problems on a large scale.
442 citations
TL;DR: In this article, an overview of evolutionary algorithms (EAs) as applied to the solution of inverse scattering problems is presented, focusing on the use of different population-based optimization algorithms for the reconstruction of unknown objects embedded in an inaccessible region when illuminated by a set of microwaves.
Abstract: This review is aimed at presenting an overview of evolutionary algorithms (EAs) as applied to the solution of inverse scattering problems. The focus of this work is on the use of different population-based optimization algorithms for the reconstruction of unknown objects embedded in an inaccessible region when illuminated by a set of microwaves. Starting from a general description of the structure of EAs, the classical stochastic operators responsible for the evolution process are described. The extension to hybrid implementations when integrated with local search techniques and the exploitation of the 'domain knowledge', either a priori obtained or collected during the optimization process, are also presented. Some theoretical discussions concerned with the convergence issues and a sensitivity analysis on the parameters influencing the stochastic process are reported as well. Successively, a review on how various researchers have applied or customized different evolutionary approaches to inverse scattering problems is carried out ranging from the shape reconstruction of perfectly conducting objects to the detection of the dielectric properties of unknown scatterers up to applications to sub-surface or biomedical imaging. Finally, open problems and envisaged developments are discussed.
439 citations
TL;DR: This paper presents an effective implementation of k-opt in LKH-2, a variant of the Lin–Kernighan TSP heuristic, and demonstrates the effectiveness of the implementation with experiments on Euclidean instances ranging from 10,000 to10,000,000 cities.
Abstract: Local search with k-exchange neighborhoods, k-opt, is the most widely used heuristic method for the traveling salesman problem (TSP). This paper presents an effective implementation of k-opt in LKH-2, a variant of the Lin–Kernighan TSP heuristic. The effectiveness of the implementation is demonstrated with experiments on Euclidean instances ranging from 10,000 to 10,000,000 cities. The runtime of the method increases almost linearly with the problem size. LKH-2 is free of charge for academic and non-commercial use and can be downloaded in source code.
360 citations
TL;DR: A simple, fast and effective iterated local search meta-heuristic to solve the TOPTW and an insert step is combined with a shake step to escape from local optima, produces a heuristic that performs very well on a large and diverse set of instances.
303 citations
TL;DR: A novel probabilistic memetic framework is presented that models MAs as a process involving the decision of embracing the separate actions of evolution or individual learning and analyzing the probability of each process in locating the global optimum.
Abstract: Memetic algorithms (MAs) represent one of the recent growing areas in evolutionary algorithm (EA) research. The term MAs is now widely used as a synergy of evolutionary or any population-based approach with separate individual learning or local improvement procedures for problem search. Quite often, MAs are also referred to in the literature as Baldwinian EAs, Lamarckian EAs, cultural algorithms, or genetic local searches. In the last decade, MAs have been demonstrated to converge to high-quality solutions more efficiently than their conventional counterparts on a wide range of real-world problems. Despite the success and surge in interests on MAs, many of the successful MAs reported have been crafted to suit problems in very specific domains. Given the restricted theoretical knowledge available in the field of MAs and the limited progress made on formal MA frameworks, we present a novel probabilistic memetic framework that models MAs as a process involving the decision of embracing the separate actions of evolution or individual learning and analyzing the probability of each process in locating the global optimum. Further, the framework balances evolution and individual learning by governing the learning intensity of each individual according to the theoretical upper bound derived while the search progresses. Theoretical and empirical studies on representative benchmark problems commonly used in the literature are presented to demonstrate the characteristics and efficacies of the probabilistic memetic framework. Further, comparisons to recent state-of-the-art evolutionary algorithms, memetic algorithms, and hybrid evolutionary-local search demonstrate that the proposed framework yields robust and improved search performance.
222 citations
TL;DR: The proposed ACS algorithm uses a construction rule as well as two multi-route local search schemes to solve the vehicle routing problem with simultaneous delivery and pickup (VRPSDP) which is a combinatorial optimization problem.
196 citations
TL;DR: Guided local search (GLS) is used to improve two of the proposed heuristics of the literature and an extra heuristic is added to regularly diversify the search in order to explore more areas of the solution space.
186 citations
24 Apr 2009
TL;DR: A group of strategies with multi-stage linearly-decreasing inertia weight (MLDW) is proposed in order to get better balance between the global and local search.
Abstract: The inertia weight is often used to control the global exploration and local exploitation abilities of particle swarm optimizers (PSO). In this paper, a group of strategies with multi-stage linearly-decreasing inertia weight (MLDW) is proposed in order to get better balance between the global and local search. Six most commonly used benchmarks are used to evaluate the MLDW strategies on the performance of PSOs. The results suggest that the PSO with W5 strategy is a good choice for solving unimodal problems due to its fast convergence speed, and the CLPSO with W5 strategy is more suitable for solving multimodal problems. Also, W5-CLPSO can be used as a robust algorithm because it is not sensitive to the complexity of problems for solving.
185 citations
TL;DR: Experimental results show that MAENS is superior to a number of state-of-the-art algorithms, and the advanced performance ofMAENS is mainly due to the MS operator, which is capable of searching using large step sizes and is less likely to be trapped in local optima.
Abstract: The capacitated arc routing problem (CARP) has attracted much attention during the last few years due to its wide applications in real life. Since CARP is NP-hard and exact methods are only applicable to small instances, heuristic and metaheuristic methods are widely adopted when solving CARP. In this paper, we propose a memetic algorithm, namely memetic algorithm with extended neighborhood search (MAENS), for CARP. MAENS is distinct from existing approaches in the utilization of a novel local search operator, namely Merge-Split (MS). The MS operator is capable of searching using large step sizes, and thus has the potential to search the solution space more efficiently and is less likely to be trapped in local optima. Experimental results show that MAENS is superior to a number of state-of-the-art algorithms, and the advanced performance of MAENS is mainly due to the MS operator. The application of the MS operator is not limited to MAENS. It can be easily generalized to other approaches.
184 citations
TL;DR: The literature where GRASP is applied to scheduling, routing, logic, partitioning, location, graph theory, assignment, manufacturing, transportation, telecommunications, biology and related fields, automatic drawing, power systems, and VLSI design is covered.
TL;DR: A metaheuristic based on simulated annealing which strikes a compromise between intensification and diversification mechanisms to augment the competitive performance of the proposed SA is applied.
Abstract: In this communication, we strive to apply a novel simulated annealing to consider scheduling hybrid flowshop problems to minimize both total completion time and total tardiness. To narrow the gap between the theory and the practice of the hybrid flowshop scheduling, we integrate two realistic and practical assumptions which are sequence-dependent setup and transportation times into our problem. We apply a metaheuristic based on simulated annealing (SA) which strikes a compromise between intensification and diversification mechanisms to augment the competitive performance of our proposed SA. A comprehensive calibration of different parameters and operators are done. We employ Taguchi method to select the optimum parameters with the least possible number of experiments. For the purpose of performance evaluation of our proposed algorithm, we generate a benchmark against which the adaptations of high performing algorithms in the literature are brought into comparison. Moreover, we investigate the impacts of increase of number of jobs on the performance of our algorithm. The efficiency and effectiveness of our hybrid simulated annealing are inferred from all the computational results obtained in various situations.
TL;DR: A metaheuristic algorithm is proposed which incorporates the rationale of Tabu Search and Guided Local Search and employs a memory structure to record the loading feasibility information of the Capacitated Vehicle Routing Problem with two-dimensional loading constraints.
TL;DR: The proposed DDE algorithm is superior to a recently published hybrid differential evolution (HDE) algorithm and the well-known multi-objective genetic local search algorithm (IMMOGLS2) in terms of searching quality, diversity level, robustness and efficiency.
TL;DR: The efficiency of the proposed SFLSDE seems to be very high especially for large scale problems and complex fitness landscapes and three other modern DE based metaheuristic for a large and varied set of test problems.
Abstract: This paper proposes the scale factor local search differential evolution (SFLSDE). The SFLSDE is a differential evolution (DE) based memetic algorithm which employs, within a self-adaptive scheme, two local search algorithms. These local search algorithms aim at detecting a value of the scale factor corresponding to an offspring with a high performance, while the generation is executed. The local search algorithms thus assist in the global search and generate offspring with high performance which are subsequently supposed to promote the generation of enhanced solutions within the evolutionary framework. Despite its simplicity, the proposed algorithm seems to have very good performance on various test problems. Numerical results are shown in order to justify the use of a double local search instead of a single search. In addition, the SFLSDE has been compared with a standard DE and three other modern DE based metaheuristic for a large and varied set of test problems. Numerical results are given for relatively low and high dimensional cases. A statistical analysis of the optimization results has been included in order to compare the results in terms of final solution detected and convergence speed. The efficiency of the proposed algorithm seems to be very high especially for large scale problems and complex fitness landscapes.
TL;DR: Two memetic algorithms (genetic algorithms hybridized with a local search) able to solve both the VFMP and the HVRP are presented, based on chromosomes encoded as giant tours, without trip delimiters and on an optimal evaluation procedure.
TL;DR: A novel chaotic PSO combined with an implicit filtering (IF) local search method to solve economic dispatch problems using chaos mapping using Henon map sequences which increases its convergence rate and resulting precision.
Abstract: Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm driven by the simulation of a social psychological metaphor instead of the survival of the fittest individual. Based on the chaotic systems theory, this paper proposed a novel chaotic PSO combined with an implicit filtering (IF) local search method to solve economic dispatch problems. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed PSO introduces chaos mapping using Henon map sequences which increases its convergence rate and resulting precision. The chaotic PSO approach is used to produce good potential solutions, and the IF is used to fine-tune of final solution of PSO. The hybrid methodology is validated for a test system consisting of 13 thermal units whose incremental fuel cost function takes into account the valve-point loading effects. Simulation results are promising and show the effectiveness of the proposed approach.
TL;DR: Genetic Design through Local Search (GDLS), a scalable, heuristic, algorithmic method that employs an approach based on local search with multiple search paths, which results in effective, low‐complexity search of the space of genetic manipulations.
Abstract: In the past decade, computational methods have been shown to be well suited to unraveling the complex web of metabolic reactions in biological systems. Methods based on flux–balance analysis (FBA) and bi-level optimization have been used to great effect in aiding metabolic engineering. These methods predict the result of genetic manipulations and allow for the best set of manipulations to be found computationally. Bi-level FBA is, however, limited in applicability because the required computational time and resources scale poorly as the size of the metabolic system and the number of genetic manipulations increase. To overcome these limitations, we have developed Genetic Design through Local Search (GDLS), a scalable, heuristic, algorithmic method that employs an approach based on local search with multiple search paths, which results in effective, low-complexity search of the space of genetic manipulations. Thus, GDLS is able to find genetic designs with greater in silico production of desired metabolites than can feasibly be found using a globally optimal search and performs favorably in comparison with heuristic searches based on evolutionary algorithms and simulated annealing.
01 Jan 2009
TL;DR: This chapter proposes new VRP heuristics based on Iterated Local Search: a pure ILS, a version with several offspring solutions per generation, called Evolutionary Local Search or ELS, and hybrid forms GRasP×ILS and GRASP×ELS, which improves two best-known solutions.
Abstract: This chapter proposes new VRP heuristics based on Iterated Local Search (ILS): a pure ILS, a version with several offspring solutions per generation, called Evolutionary Local Search or ELS, and hybrid forms GRASP×ILS and GRASP×ELS. These variants share three main features: a simple structure, an alternation between solutions encoded as giant tours and VRP solutions, and a fast local search based on a sequential decomposition of moves. The proposed methods are tested on the Christofides et al. (1979) and Golden et al. (1998) instances. Our best algorithm is the GRASP×ELS hybrid. On the first set, if only one run with the same parameters is allowed, it outperforms all recent heuristics except the AGES algorithm of Mester and Braysy (2007). Only AGES and the SEPAS method of Tarantilis (2005) do better on the second set, but GRASP×ELS improves two best-known solutions. Our algorithm is also faster than most VRP metaheuristics.
TL;DR: This research is the first application of immune algorithm to the optimization of machining parameters in turning and also shape design optimization problems in the literature, and demonstrates the superiority of the proposed hybrid over the other techniques in terms of solution quality and convergence rates.
06 Mar 2009
TL;DR: An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-basedHill climbing, to address the convergence problem.
Abstract: Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of memetic algorithms, which combines the features of greedy crossover-based hill climbing and steepest mutation-based hill climbing. In order to address the convergence problem, two diversity maintaining methods, called adaptive dual mapping and triggered random immigrants, respectively, are also introduced into the proposed memetic algorithm for dynamic optimization problems. Based on a series of dynamic problems generated from several stationary benchmark problems, experiments are carried out to investigate the performance of the proposed memetic algorithm in comparison with some peer evolutionary algorithms. The experimental results show the efficiency of the proposed memetic algorithm in dynamic environments.
TL;DR: A memetic algorithm for solving JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints is developed and results show that MA, as compared to GA, not only improves the quality of solutions but also reduces the overall computational time.
Abstract: The job-shop scheduling problem is well known for its complexity as an NP-hard problem. We have considered JSSPs with an objective of minimizing makespan while satisfying a number of hard constraints. In this paper, we developed a memetic algorithm (MA) for solving JSSPs. Three priority rules were designed, namely partial re-ordering, gap reduction and restricted swapping, and used as local search techniques in our MA. We have solved 40 benchmark problems and compared the results obtained with a number of established algorithms in the literature. The experimental results show that MA, as compared to GA, not only improves the quality of solutions but also reduces the overall computational time.
11 Jul 2009
TL;DR: This work introduces a generalised, highly parameterised solver framework, dubbed SATenstein, that includes components gleaned from or inspired by existing high-performance SLS algorithms for SAT, and configuration procedure to find instantiations of SATenstein that perform well on several well-known, challenging distributions of SAT instances.
Abstract: Designing high-performance algorithms for computationally hard problems is a difficult and often time-consuming task. In this work, we demonstrate that this task can be automated in the context of stochastic local search (SLS) solvers for the propositional satisfiability problem (SAT). We first introduce a generalised, highly parameterised solver framework, dubbed SATenstein, that includes components gleaned from or inspired by existing high-performance SLS algorithms for SAT. The parameters of SATenstein control the selection of components used in any specific instantiation and the behaviour of these components. SATenstein can be configured to instantiate a broad range of existing high-performance SLS-based SAT solvers, and also billions of novel algorithms. We used an automated algorithm configuration procedure to find instantiations of SATenstein that perform well on several well-known, challenging distributions of SAT instances. Overall, we consistently obtained significant improvements over the previously best-performing SLS algorithms, despite expending minimal manual effort.
08 Jun 2009
TL;DR: In this work, approximation algorithms for maximum independent set of pseudo-disks in the plane, both in the weighted and unweighted cases are presented, and it is proved that a local search algorithm yields a PTAS.
Abstract: We present approximation algorithms for maximum independent set of pseudo-disks in the plane, both in the weighted and unweighted cases. For the unweighted case, we prove that a local search algorithm yields a PTAS. For the weighted case, we suggest a novel rounding scheme based on an LP relaxation of the problem, that leads to a constant-factor approximation. Most previous algorithms for maximum independent set (in geometric settings) relied on packing arguments that are not applicable in this case. As such, the analysis of both algorithms requires some new combinatorial ideas, which we believe to be of independent interest.
TL;DR: A genetic algorithm (GA) based heuristic is designed and implemented that reaches the best-known solution 14 times and finds one new best solution and provides a competitive performance in terms of average solution.
Abstract: This paper studies the fleet size and mix vehicle routing problem (FSMVRP), in which the fleet is heterogeneous and its composition to be determined. We design and implement a genetic algorithm (GA) based heuristic. On a set of twenty benchmark problems it reaches the best-known solution 14 times and finds one new best solution. It also provides a competitive performance in terms of average solution.
TL;DR: Two VNS variants, which differ in the order the diversification and Dijkstra's algorithm are used, are implemented and appear to be competitive and produce new best results when tested on the data sets from the literature.
TL;DR: A hybrid metaheuristic for the minimization of makespan in scheduling problems with parallel machines and sequence-dependent setup times by hybridization of an ACO, SA with VNS, combining the advantages of these three individual components.
Abstract: This paper proposes a hybrid metaheuristic for the minimization of makespan in scheduling problems with parallel machines and sequence-dependent setup times. The solution approach is robust, fast, and simply structured, and comprises three components: an initial population generation method based on an ant colony optimization (ACO), a simulated annealing (SA) for solution evolution, and a variable neighborhood search (VNS) which involves three local search procedures to improve the population. The hybridization of an ACO, SA with VNS, combining the advantages of these three individual components, is the key innovative aspect of the approach. Two algorithms of a hybrid VNS-based algorithm, SA/VNS and ACO/VNS, and the VNS algorithm presented previously are used to compare with the proposed hybrid algorithm to highlight its advantages in terms of generality and quality for large instances.
TL;DR: An improved multiobjective particle swarm optimization (MOPSO) algorithm is developed to derive a set of Pareto-optimal solutions and local search is used to increase its search efficiency in the proposed version of MOPSO.
TL;DR: This paper compared SFLK-means with other heuristics algorithm in clustering, such as GAK, SA, TS, and ACO, by implementing them on several simulations and real datasets and shows that the proposed algorithm works better than others.
Abstract: Evolutionary algorithms, such as shuffled frog leaping, are stochastic search methods that mimic natural biological evolution and/or the social behavior of species. Such algorithms have been developed to arrive at near-optimum solutions to complex and large-scale optimization problems which cannot be solved by gradient-based mathematical programming techniques. The shuffled frog-leaping algorithm draws its formulation from two other search techniques: the local search of the “particle swarm optimization” technique and the competitiveness mixing of information of the “shuffled complex evolution” technique. Cluster analysis is one of the attractive data mining techniques which is used in many fields. One popular class of data clustering algorithms is the center-based clustering algorithm. K-means is used as a popular clustering method due to its simplicity and high speed in clustering large datasets. However, k-means has two shortcomings: Dependency on the initial state and convergence to local optima and global solutions of large problems cannot be found with reasonable amount of computation effort. In order to overcome local optima problem, lots of studies are done in clustering. In this paper, we proposed an application of shuffled frog-leaping algorithm in clustering (SFLK-means). We compared SFLK-means with other heuristics algorithm in clustering, such as GAK, SA, TS, and ACO, by implementing them on several simulations and real datasets. Our finding shows that the proposed algorithm works better than others.
TL;DR: This study presents a type II robotic assembly line balancing (rALB-II) problem, in which the assembly tasks have to be assigned to workstations, and each workstation needs to select one of the available robots to process the assigned tasks with the objective of minimum cycle time.