Efficient Local Search Procedure Research Articles

MEMPSODE: A global optimization software based on hybridization of population-based algorithms and local searches

We present MEMPSODE, a global optimization software tool that integrates two prominent population-based stochastic algorithms, namely Particle Swarm Optimization and Differential Evolution, with well established efficient local search procedures made available via the Merlin optimization environment. The resulting hybrid algorithms, also referred to as Memetic Algorithms, combine the space exploration advantage of their global part with the efficiency asset of the local search, and as expected they have displayed a highly efficient behavior in solving diverse optimization problems. The proposed software is carefully parametrized so as to offer complete control to fully exploit the algorithmic virtues. It is accompanied by comprehensive examples and a large set of widely used test functions, including tough atomic cluster and protein conformation problems. Program summaryProgram title: MEMPSODE (MEMetic Particle Swarm Optimization and Differential Evolution)Catalogue identifier: AELM_v1_0Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AELM_v1_0.htmlProgram obtainable from: CPC Program Library, Queenʼs University, Belfast, N. IrelandLicensing provisions: Standard CPC license, http://cpc.cs.qub.ac.uk/licence/licence.htmlNo. of lines in distributed program, including test data, etc.: 14 877No. of bytes in distributed program, including test data, etc.: 592 244Distribution format: tar.gzProgramming language: ANSI C, ANSI Fortran-77Computer: WorkstationsOperating system: Developed under the Linux operating system using the GNU compilers v.4.4.3. It has also been tested under Solaris and the Cygwin environment.RAM: The code uses O(n×N) internal storage, n being the dimension of the problem and N the maximum population size. The required memory is dynamically allocated.Word size: 64 bitsClassification: 4.9Subprograms used:Cat IdTitleReferenceAAXW_v4_0 MERLIN-3.1.1 CPC 159 (2004) 70Nature of problem: Optimization is a valuable mathematical tool for solving a plethora of scientific and engineering problems. Usually, the underlying problems are modeled with objective functions whose minimizers (or maximizers) correspond to the desired solutions of the original problem. In many cases, there is a multitude of such minimizers that correspond to solutions either locally, i.e., in their close neighborhood, or globally, i.e., with respect to the whole search space. There is a significant number of efficient algorithms for addressing optimization problems. One can distinguish two main categories, based on their adequacy in performing better global (exploration) or local (exploitation) search. Standard local optimization algorithms have the ability to rapidly converge towards local minimizers but they are also prone to get easily trapped in their vicinity. These algorithms usually exploit local information of the objective function, including first- and second-order derivatives. On the other hand, global optimization algorithms are designed to perform better exploration, although at the cost of questionable convergence properties. Typically, these approaches integrate stochastic operations. The form of the optimization problem at hand plays a crucial role in the selection of the most appropriate algorithm. Objective functions that lack nice mathematical properties (such as differentiability, continuity etc.) may raise applicability issues for algorithms that require derivatives. On the other hand, applications that require high accuracy may be laborious for stochastic algorithms. The existence of a multitude of local and/or global minimizers can render these problems even harder for any single optimization algorithm.Solution method: Evolutionary Algorithms and Swarm Intelligence approaches have been established as effective global optimization algorithms that make minor assumptions about the objective function. Particle Swarm Optimization (PSO) and Differential Evolution (DE) possess a salient position among the most successful algorithms of these categories. Numerous studies indicate that their performance can be radically improved when combined with efficient local optimization schemes. The resulting hybrid algorithms offer more balanced search intensification/diversification than the original ones, thereby increasing both their efficiency and effectiveness. Such hybrid schemes are called Memetic Algorithms, and they have gained a rapidly growing interest over the past few years.We present MEMPSODE (MEMetic, PSO and DE), a global optimization software that implements memetic PSO and DE within a unified framework. The software utilizes local search procedures from the established Merlin optimization environment. The performance of the implemented approaches is illustrated on several examples, including hard optimization tasks such as atomic cluster and protein conformation problems.Restrictions: The current version of the software uses double precision arithmetic. However, it can be easily adapted by the user to handle integer or mixed-integer problems.Unusual features: The software takes into account only bound constraints. General constraints may be tackled by user-defined penalty or barrier functions that can be easily incorporated in the source code of the objective function.Additional comments: The use of the Merlin Optimization Environment 3.1.1 (see subprograms above) is optional.A comprehensive user manual is provided that covers in detail the installation procedure and provides detailed examples of operation.Running time: The running time depends solely on the complexity of the objective function (and its derivatives, if used) as well as on the available computational budget (number of function evaluations). The test run provided (Rastrigin function n=10), requires 2.5×106 function evaluations (2.8 seconds on an i7-920 CPU).

Angel method for discrete optimization problems

In this study we present an efficient new hybrid meta-heuristic - named in other context ANGEL - for solving discrete size optimization of truss structures. ANGEL combines ant colony optimization (ACO), genetic algorithm (GA) and local search (LS) strategy. The procedures of ANGEL attempt to solve an optimization problem by repeating the following steps. First time, ACO searches the solution space and generates structure designs to provide the initial population for GA. After that, GA is executed and the pheromone set in ACO is updated when GA obtains a better solution. When GA terminates, ACO searches again by using the new pheromone set. ACO and GA search alternately and cooperatively in the solution space. In this study we propose an efficient local search procedure. The procedure, in an iterative process, tries to make a better (a lighter feasible or a less unfeasible) truss from the current truss obtained by ACO or GA. The geometrically and materially nonlinear space trusses are formulated as a large d isplacement structural model. The treatment of elastic-plastic collapse analysis is based on a path-following method \cite 6. The applied method is a combination of the perturbation technique of the stability theory and the non-linear modification of the classical linear homotopy method. With the help of the higher-order predictor-corrector terms, the method is able to follow the load- deflection path even in case of elastic-plastic material law.

A hybrid metaheuristic for the resource-constrained project scheduling problem

This study presents a hybrid metaheuristic ANGEL for the resource-constrained project scheduling problem (RCPSP). ANGEL combines ant colony optimization (ACO), genetic algorithm (GA) and local search strategy. The procedures of ANGEL are as follows. First, ACO searches the solution space and generates activity lists to provide the initial population for GA. Next, GA is executed and the pheromone set in ACO is updated when GA obtains a better solution. When GA terminates, ACO searches again by using a new pheromone set. ACO and GA search alternately and cooperatively in the solution space. This study also proposes an efficient local search procedure which is applied to yield a better solution when ACO or GA obtains a solution. A final search is applied upon the termination of ACO and GA. The experimental results of ANGEL on the standard sets of the project instances show that ANGEL is an effective method for solving the RCPSP.

Variable neighborhood search for the vertex weighted k-cardinality tree problem

This paper presents some new heuristics based on variable neighborhood search to solve the vertex weighted k-cardinality tree problem. An efficient local search procedure is also developed for use within these heuristics. Our computational results demonstrate that the new heuristics substantially outperform the state-of-the-art methodologies, including a tabu search and genetic algorithm recently proposed in the literature. We also show that a decomposition approach is best for larger problem sizes than previously investigated. Thus, our findings advance in a significant way the capacity to solve this important class of problems.

UMTS radio network evaluation and optimization beyond snapshots

A new evaluation scheme for universal mobile telecommunications system (UMTS) radio networks is introduced. The approach takes the complex coupling of coverage and capacity through interference into account. Cell load estimates, otherwise obtained through Monte-Carlo simulation, can now be approximated without time-consuming iterative simulations on user snapshots. The two cornerstones are the generalization of interference coupling matrices from user snapshots to average load and the emulation of load control by an analytical scaling scheme. Building on the new evaluation scheme, two novel radio network optimization algorithms are presented: an efficient local search procedure and a mixed integer program that aims at designing the coupling matrix. Computational experiments for optimizing antenna tilts show that our new approaches outperform traditional snapshot models

A Complete and Efficient Algorithm for Searching 3-D Form-Closure Grasps in the Discrete Domain

A complete and efficient algorithm is proposed for searching form-closure grasps of n hard fingers on the surface of a three-dimensional object represented by discrete points. Both frictional and frictionless cases are considered. This algorithm starts to search a form-closure grasp from a randomly selected grasp using an efficient local search procedure until encountering a local minimum. The local search procedure employs the powerful ray-shooting technique to search in the direction of reducing the distance between the convex hull corresponding to the grasp and the origin of the wrench space. When the distance reaches a local minimum in the local search procedure, the algorithm decomposes the problem into a few subproblems in subsets of the points according to the existence conditions of form-closure grasps. A search tree whose root represents the original problem is employed to perform the searching process. The subproblems are represented as children of the root node and the same procedure is recursively applied to the children. It is proved that the search tree generates O(KlnK/n) nodes in case a from-closure grasp exists, where K is the number of the local minimum points of the distance in the grasp space and n is the number of fingers. Compared to the exhaustive search, this algorithm is more efficient, and, compared to other heuristic algorithms, the proposed algorithm is complete in the discrete domain. The efficiency of this algorithm is demonstrated by numerical examples.

Minimizing energy below the glass thresholds

Focusing on the optimization version of the random K -satisfiability problem, the MAX- K -SAT problem, we study the performance of the finite energy version of the survey propagation algorithm. We show that a simple (linear time) backtrack decimation strategy is sufficient to reach configurations well below the lower bound for the dynamic threshold energy and very close to the analytic prediction for the optimal ground states. A comparative numerical study on one of the most efficient local search procedures is also given.

Final assembly sequencing for just-in-time manufacturing

SUMMARY Efficient sequencing of a mix of end-products in a final assembly shop is particularly well suited for a pull manufacturing environment, such as just-in-time (JIT). Since each product imposes different requirements on the feeder shops, in terms of parts' production, the feeder shops are driven by the sequence of final assembly. We developed a mathematical model and an algorithm for the final-assembly sequencing (FAS) problem. Our model links the FAS problem to smoothing the work-in-process levels of parts in the feeder shops. We designed an efficient local search procedure, based on selective pairwise interchanges, to solve a lexicographic minimax formulation of the FAS problem. Extensive testing revealed that the algorithm consistently provided excellent results in a tractable running time.