Solving Three-Objective Flow Shop Problem with Fast Hypervolume-Based Local Search Algorithm

This brief considers the Direction of Arrival (DOA) estimation for Distributed mmWave Radar System (DmmWRS) in the presence of coherent sources and a single snapshot. To this end, the large virtual aperture is firstly derived assuming that DmmWRS works in time-sharing mode. Then, a Fast Local Iterative Search Algorithm (FLISA) resorting to Iterative Adaptive Approach (IAA) and prior DOA information obtained by a single radar, is developed to accurately estimate DOA within a small azimuth region of interest. Finally, numerical simulations and actual measurements show the superiority of the proposed approach than some counterparts providing better angle measurement performance, more importantly, FLISA has lower computational complexity and faster convergence than IAA.

Communication and topology aware process mapping is a powerful approach to reduce communication time in parallel applications with known communication patterns on large, distributed memory systems. We address the problem as a quadratic assignment problem (QAP), and present algorithms to construct initial mappings of processes to processors as well as fast local search algorithms to further improve the mappings. By exploiting assumptions that typically hold for applications and modern supercomputer systems such as sparse communication patterns and hierarchically organized communication systems, we arrive at significantly more powerful algorithms for these special QAPs. Our multilevel construction algorithms employ recently developed, perfectly balanced graph partitioning techniques and excessively exploit the given communication system hierarchy. We present improvements to a local search algorithm of Brandfass et al. (2013), and decrease the running time by reducing the time needed to perform swaps in the assignment as well as by carefully constraining local search neighborhoods. Experiments indicate that our algorithms not only dramatically speed up local search, but due to the multilevel approach also find much better solutions in practice.

An optimization algorithm is presented which consists of cyclically heating\nand quenching by Metropolis and local search procedures, respectively. It works\nparticularly well when it is applied to an archive of samples instead of to a\nsingle one. We demonstrate for the traveling salesman problem that this\nalgorithm is far more efficient than usual simulated annealing; our\nimplementation can compete concerning speed with recent, very fast genetic\nlocal search algorithms.\n

Recently, the property of connectedness has been claimed to give a strong motivation on the design of local search techniques for multiobjective combinatorial optimization. Indeed, when connectedness holds, a basic Pareto local search, initialized with at least one non-dominated solution, allows to identify the efficient set exhaustively. However, this becomes quickly infeasible in practice as the number of efficient solutions typically grows exponentially with the instance size. As a consequence, we generally have to deal with a limited-size approximation, ideally a representative sample of efficient solutions. In this paper, we propose the biobjective long and multiple path problems. We show experimentally that, on the first problem, even if the efficient set is connected, a local search may be outperformed by a simple evolutionary algorithm in the sampling of the efficient set. At the opposite, on the second problem, a local search algorithm may successfully approximate a disconnected efficient set. Then, we argue that connectedness is not the single property to study for the design of multiobjective local search algorithms. This work opens new discussions on a proper definition of multiobjective fitness landscapes.KeywordsLocal SearchProblem InstancePareto FrontObjective SpaceLocal Search AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper investigates the optimal access point (AP) placement (OAPP) in simultaneous broadcast system using orthogonal frequency division multiplexing (OFDM) for high data rate indoor wireless LAN. We aim to determine the best combination of multiple AP placements that minimizes average bit error rate (BER) of terminals when in put information including an indoor configuration, a number APs, and total transmission power is given a priori. Since OAPP is categorized as a hard combinatorial problem with many variables and constraints, we applied a nonlinear optimization scheme called uniform design simulated annealing (UDSA) to search for an approximate optimal solution and analyzed its performance in detail. The simulation results demonstrate that the UDSA algorithm has been successfully applied to OAPP and its effectiveness was also confirmed in comparison with very fast simulated annealing (VFSA) and local search (LS) algorithms.

In intensity-modulated radiation therapy, treatment planners aim to irradiate the tumour according to a medical prescription while sparing surrounding organs at risk as much as possible. Although this problem is inherently a multi-objective optimisation (MO) problem, most of the models in the literature are single-objective ones. For this reason, a large number of single-objective algorithms have been proposed in the literature to solve such single-objective models rather than multi-objective ones. Further, a difficulty that one has to face when solving the MO version of the problem is that the algorithms take too long before converging to a set of (approximately) non-dominated points. In this paper, we propose and compare three different strategies, namely random PLS (rPLS), judgement-function-guided PLS (jPLS) and neighbour-first PLS (nPLS), to accelerate a previously proposed Pareto local search (PLS) algorithm to solve the beam angle selection problem in IMRT. A distinctive feature of these strategies when compared to the PLS algorithms in the literature is that they do not evaluate their entire neighbourhood before performing the dominance analysis. The rPLS algorithm randomly chooses the next non-dominated solution in the archive and it is used as a baseline for the other implemented algorithms. The jPLS algorithm first chooses the non-dominated solution in the archive that has the best objective function value. Finally, the nPLS algorithm first chooses the solutions that are within the neighbourhood of the current solution. All these strategies prevent us from evaluating a large set of BACs, without any major impairment in the obtained solutions’ quality. We apply our algorithms to a prostate case and compare the obtained results to those obtained by the PLS from the literature. The results show that algorithms proposed in this paper reach a similar performance than PLS and require fewer function evaluations.

Multi-objective local search (MOLS) algorithms are efficient meta-heuristics, which improve a set of solutions by using their neighbourhood to iteratively find better and better solutions. MOLS algorithms are versatile algorithms with many available strategies, first to select the solutions to explore, then to explore them, and finally to update the archive using some of the visited neighbours. In this paper, we propose a new generalisation of MOLS algorithms incorporating new recent ideas and algorithms. To be able to instantiate the many MOLS algorithms of the literature, our generalisation exposes numerous numerical and categorical parameters, raising the possibility of being automatically designed by an automatic algorithm configuration (AAC) mechanism. We investigate the worth of such an automatic design of MOLS algorithms using MO-ParamlLS, a multi-objective AAC configurator, on the permutation flowshop scheduling problem, and demonstrate its worth against a traditional manual design.

A Variable Iterated Local Search Algorithm for Energy-Efficient No-idle Flowshop Scheduling Problem

Maximum satisfiability problem is the central problem in theoretical computer science.Maximum 3-satisfiability problem(Max-(3)-Sat) is one of the most important sub problem of the maximum satisfiablity problem.Local search has been testified to be very effective for solving this problem by many times of real computation.However,there is no related results for analyzing the performance of the local search method to solve the problem.This paper approaches the local search method to solve the maximum satisfiablity problems and analyzes the performance of the method.The central issue of the paper is to discuss the local search algorithm to solve the Max-(3)-Sat.The authors show that the one-bit-flip local search algorithm can achieve the performance ratio 4/3 for solving the Max-(3)-Sat;and the algorithm of one-bit-flip local search following the conditionally all-bits-flip can achieve the performance ratio 5/4 for solving Max-(3)-Sat.And the authors design a new local search algorithm to solve the Max-(3)-Sat problem using the one-bit-flip local search following the all-bits-flip,and show the new algorithm have the performance ratio 8/7.The 8/7-approximation algorithm can be extended to solve the Max-(k)-Sat problem with performance ratio (2k+2)/(2k+1) for k4.Finally the authors give the two-bits-flip local search algorithm to solve the Max-(k)-Sat problem,and prove the algorithm can have the performance ratio 1+12k+1+k(k-1)/(n-k) for k4.Repeatedly running the local search algorithm can further improve the performance of the solution.The results of the paper demonstrate the inevitability for local search to have high performances in solving the maximum satisifiablity problems.

Recently, scalarizing-function based approaches have received a renewed interest in multi-objective optimization. In fact, the simplicity of these functions allows them to be easily adaptable to any resolution method. In this contribution, we propose an iterated multi-objective local search algorithm, called MoLS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AugWT</sup> , based on well-known and frequently-used scalarizing-function: the augmented weighted Chebyshev function. A neighborhood structure employing a ranking algorithm based on a weighted addition ratio is introduced in this work. The proposed ranking algorithm is integrated in MoLS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AugWT</sup> in order to try to establish an order between the candidate items. It has the goal not only to focus the search on the most promising areas of the search space, thus to improve the quality of the obtained solutions, but also to help the population to converge while diminishing the computational time during the optimization process. To evaluate the performance of the proposed algorithm, MoLS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AugWT</sup> is applied to the multi-objective multidimensional knapsack problem (MOMKP). Experimental results have shown the efficiency of MoLS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AugWT</sup> compared to the state-of the-art algorithms on nine well-known benchmark instances of MOMKP.

Top-k proximity query in large graphs is a fundamental problem with a wide range of applications. Various random walk based measures have been proposed to measure the proximity between different nodes. Although these measures are effective, efficiently computing them on large graphs is a challenging task. In this paper, we develop an efficient and exact local search method, FLoS (Fast Local Search), for top-k proximity query in large graphs. FLoS guarantees the exactness of the solution. Moreover, it can be applied to a variety of commonly used proximity measures. FLoS is based on the no local optimum property of proximity measures. We show that many measures have no local optimum. Utilizing this property, we introduce several operations to manipulate transition probabilities and develop tight lower and upper bounds on the proximity values. The lower and upper bounds monotonically converge to the exact proximity value when more nodes are visited. We further extend FLoS to measures having local optimum by utilizing relationship among different measures. We perform comprehensive experiments on real and synthetic large graphs to evaluate the efficiency and effectiveness of the proposed method.

Local search algorithms are widely adopted in solving large-scale Distributed Constraint Optimization Problems (DCOPs). However, since each agent always makes its value decision based on the values of its neighbors in local search, those algorithms usually suffer from local premature convergence. More concretely, an agent cannot make a wise decision with poor values of its neighbors since its decision space is bound up with those poor values. In this paper, we propose a Partial Decision Scheme (PDS) to relax the decision space of an agent by ignoring the value of its neighbor which has the bad impact on its local benefits. The PDS comprises two partial decision processes: trigger partial decision and recursive partial decision. The former is iteratively performed by agents who cannot enhance their local benefits unilaterally to break out of potential local optima. The latter is recursively performed by neglected agents to improve global benefits. Besides, the trigger conditions along with a self-adaptive probability are introduced to control the use of PDS. The PDS can be easily applied to any local search algorithm to overcome its local premature convergence with a small additional overhead. In our theoretical analysis, we prove the feasibility and convergence of PDS. Moreover, the experimental results also demonstrate the superiority of the use of PDS in the typical local search algorithms over state-of-the-art local search algorithms.

The practical design of multiple description vector quantizers for robust distributed speech recognition over packet networks is investigated. In the proposed system, speech parameters are quantized and mapped to multiple descriptions for transmission over independent channels. A new approach to the index assignment optimization is presented on the basis of a linear programming framework. Also, a fast local search algorithm is proposed to find the optimal index assignment without compromising the speech recognition accuracy. Experiments with random packet loss in a range of loss conditions are conducted on the Mandarin digit string recognition task. Simulation results indicate that the proposed multiple description scheme provides more robust performance than the ETSI standardized split vector quantization scheme with a single description.

We apply the transitionless driving on the local adiabatic quantum search algorithm to speed up the adiabatic process. By studying quantum dynamics of the adiabatic search algorithm with the equivalent two-level system, we derive the transitionless driving Hamiltonian for the local adiabatic quantum search algorithm. We found that when adding a transitionless quantum driving term HD(t) on the local adiabatic quantum search algorithm, the success rate is 1 exactly with arbitrary evolution time by solving the time-dependent Schr¨odinger equation in eigen-picture. Moreover, we show the reason for the drastic decrease of the evolution time is that the driving Hamiltonian increases the lowest eigenvalues to a maximum of .

Block motion estimation using full search is computationally intensive. Previously proposed fast algorithms reduce the computation by limiting the number of searching locations. This is accomplished at the expense of less accuracy of motion estimation and gives rise to an appreciably higher mean squared error (MSE) for motion compensated images. We present a new fast and efficient search algorithm for block motion estimation that produces better quality performance and less computational time compared with a three-step search (TSS) algorithm. The proposed algorithms are based on the ideas of the dithering pattern for pixel decimation, multiple-candidate for pixel-decimation-based full search, and center-based distribution of the motion vector. From the experimental results, the proposed algorithm is superior to the TSS in both quality performance (about 0.2 dB) and computational complexity (about half).