Effects of Parameters of an Island Model Parallel Genetic Algorithm for the Quadratic Assignment Problem

Optimization of FRP composites against impact induced failure using island model parallel genetic algorithm

The Quadratic Assignment Problem (QAP) is a difficult and important problem studied in the domain of combinatorial optimisation. It is possible to solve QAP instances with 10--20 facilities using exhaustive parallel algorithms within a few days on a cluster machine. However, large QAP instances with more than 100 facilities are not solvable using exhaustive techniques. We have explored a variety of Genetic Algorithm crossover operators for this problem and verified its performance experimentally using well-known instances from the QAPLIB library. By increasing the number of processors, generations and population sizes we have been able to find solutions that are the same as (or very close to) the best reported solutions for large QAP instances in QAPLIB. In order to parallelise the Genetic Algorithm we generate and evolve separate solution pools on each cluster processor, using an island model. This model exchanges 10% of each processor’s solutions at the initial stages of optimisation. We show experimentally that both execution times and solution qualities are improved for large QAP instances by using our Island Parallel Genetic Algorithm.

This paper presents a hybrid identification method for Takagi-Sugeno-Kang (TSK) fuzzy model by means of a combination of optimisation techniques. First, the K-means clustering algorithm is used to get information granules (centres of clusters) which are used as the initial location of apexes of the MFs in the premise and the prototypes of the polynomial functions used in the consequent parts of the fuzzy rules. Subsequently, the initial fuzzy system is evolved iteratively by means of a hybrid learning. In particular, the premise part parameters are tuned by a combination of a Island Model Parallel Genetic Algorithm (IMPGA) and a space search Memetic Algorithm (MA) while the consequent parameters of the system are derived optimally by an improved QR Householder least square method (LSM). The optimisation search algorithm (IMPGA+MA) allows exploring the search space in multiple trajectories simultaneously to avoid getting trapped in local-optimal while the improved LSM helps minimize the occurrences of the underflow and overflow problems when dealing with floating point numbers. The proposed optimisation framework can be applied for a variety of application areas such as function approximation, time-series prediction, etc. However, in this paper, the proposed method is only evaluated using the well-known Mackey-Glass time-series prediction benchmark and has shown a better prediction accuracy than any other previous works of the same problem.

A new tool for automated transformation of Quadratic Assignment Problem instances to Quadratic Unconstrained Binary Optimisation models

With the increase in the volume of container trading, automated container terminals have gradually become the target of the world’s largest port construction. In order to improve the efficiency of the automated AGV container terminal and gantry cranes, the possible conflicts in the horizontal AGV transportation area under different load conditions and speeds have been resolved. The problem is to create a comprehensive scheduling model that minimizes the total AGV execution time through prediction and solved by genetic algorithm. Experimental data analysis shows that after optimization, the total execution time and conflict timeout of multiple AGVs are reduced, which improves the work efficiency of AGVs.

The authors describe a genetic algorithm for a difficult optimisation problem which arises in the context of parallel processing. The problem is to assign each task in the given task graph T to a processor, so as to minimise the total overall execution time of the tasks. Total execution time is computed with the knowledge of individual run times of tasks and the communication requirements among tasks. The intertask communication time is dependent on the interconnection network which connects the processors. No prior knowledge of the interconnection topology is assumed. The algorithm finds the interconnection architecture that is best suited for the task graph T; this makes sense when the target architecture is reconfigurable through programmable switches, e.g. transputer based parallel processors. The algorithm is also extended to add heterogeneous platforms, where each task t can be executed on a particular class of processors. The optimisation technique is based on the genetic paradigm. The authors describe an efficient chromosome representation, genetic operators and a fitness measure suitable for the application.

Ant colony optimization is an evolutionary approach where a number of ants search for good solutions. Ant Algorithms often show good optimization results. This paper, discusses number of algorithms built using modified ant colony algorithm which are used to solve the problem of assigning each task in a given task graph to a processor in a reconfigurable multi processor architecture so as to minimize the total overall execution time of the tasks. Each group of ants is working collectively to find the interconnection architecture best suited for the task graph and the best scheduling of that task graph on the chosen interconnection architecture. The total execution time is computed according to the knowledge of individual run times of tasks and the communication requirements among tasks. The inter task communication time is dependent on the interconnection processor architecture. The results of using modified ant colony algorithm in reconfigurable parallel processor system are compared with that of using genetic algorithm for solving the same problem.

-Quadratic assignment problems (QAPs) are NP-hard combinatorial optimization problems often used to compare the performance of meta-heuristics. In this paper, we propose a QAP problem instance generator whose instances can be used as benchmark for meta-heuristics. Our generator aggregates various QAP instances into a larger size QAP instance in order to obtain an large size, additively decomposable QAP. We assume that a QAP instance that is difficult for local search has many local optima from which local search needs to escape from. We call the resulting QAPs the composite QAP instances (cQAPs). We use numerical and empirical techniques for the landscape analysis of generated composite QAPs. The comparison between our QAP instances with the other QAPs from the literature classify cQAPs as difficult. We show that heuristic algorithms that exploit the particularities of the cQAP search space, like iterated local search, can outperform other heuristics that do not consider the structure of this search space, like the multi-restart local search.

Introduction. The quadratic assignment problem is a well-established NP-hard problem in combinatorial optimization with applications in diverse fields like economics, archaeology, and chemistry. Due to its complexity, research on efficient solution methods remains active, including efforts for parallelization on multiprocessor computing systems. However, effective parallel algorithms are crucial to fully leverage these computational resources. In this context, algorithm unions (portfolios and teams) play a significant role in achieving parallel execution for solving such problems. Research objectives. This work investigates the application of portfolios constructed from modifications of the repeated iterated tabu search algorithm to the quadratic assignment problem. The effectiveness of these portfolios was evaluated through experimental computations. Results. The portfolios, derived from modifications of the repeated iterated tabu search algorithm, were applied to the quadratic assignment problem. For the most demanding test instances, the proposed algorithms were evaluated on the SCIT-4 supercomputer, alongside previously published results from the authors, confirming their competitive performance. Additionally, we assessed the parallel efficiency of these portfolios in solving instances of the quadratic assignment problem. The results demonstrate their ability to accelerate the optimization process (with speedup dependent on problem size and utilized processors), enabling the solution of large-scale problems. Conclusions. The conducted studies demonstrate that employing algorithm portfolios significantly accelerates the solution process for the quadratic assignment problem. Analysis of the results reveals a near-linear speedup factor achieved by the portfolio. For the challenging test instance tai100a, a new best solution value of 21040996 was obtained using a portfolio of 16 algorithms. Keywords: quadratic assignment problem, algorithm portfolios, experimental research, algorithm portfolios efficiency.

The efficient implementation of a diffraction tomography method for medical imaging is addressed within the framework of High Performance Computing (HPC) environment. A non-linear optimization method for the solution of the inverse scattering problem is implemented on a shared memory model computer. Linear speed-up and significant reduction in the total execution time is achieved when the program is executed in parallel, enabling the feasibility of the method for realistic medical imaging applications.

The quadratic assignment problem (QAP) is an NP-hard problem with a wide range of applications in many real-world applications. This study introduces a discrete rat swarm optimizer (DRSO)algorithm for the first time as a solution to the QAP and demonstrates its effectiveness in terms of solution quality and computational efficiency. To address the combinatorial nature of the QAP, a mapping strategy is introduced to convert real values into discrete values, and mathematical operators are redefined to make then suitable for combinatorial problems. Additionally, a solution quality improvement strategy based on local search heuristics such as 2-opt and 3-opt is proposed. Simulations with test instances from the QAPLIB test library validate the effectiveness of the DRSO algorithm, and statistical analysis using the Wilcoxon parametric test confirms its performance. Comparative analysis with other algorithms demonstrates the superior performance of DRSO in terms of solution quality, convergence speed, and deviation from the best-known values, making it a promising approach for solving the QAP.

Quadratic assignment problems (QAPs) is a NP-hard combinatorial optimization problem. QAPs are often used to compare the performance of meta-heuristics. In this paper, we propose a QAP problem instance generator that can be used for benchmarking for heuristic algorithms. Our QAP generator combines small size QAPs with known optimum solution into a larger size QAP instance. We call these instances composite QAPs (cQAPs), and we show that the cost function of cQAPs is additively decomposable. We give mild conditions for which a cQAP instance has known optimum solution. We generate cQAP instances using uniform distributions with different bounds for the component QAPs and for the rest of the cQAP elements. Numerical and analytical techniques that measure the difficulty of the cQAP instances in comparison with other QAPs from the literature are introduced. These methods point out that some cQAP instances are difficult for local search with many local optimum of various values, low epistasis and non-trivial asymptotic behaviour.

<p>The main objective of our research is to improve an adaptation of the chicken swarm optimization algorithm (CSO) to solve the quadratic assignment problem, which is a well-known combinatorial optimization problem. The new approach is based on the CSO without using a local search, the CSO-QAP is a stochastic method inspired from the behavior of chickens in swarm while searching for food. The experiments are performed on a set of 56 benchmark QAPLIB instances. To prove the robustness of our algorithm a comparative analysis is done with the known metaheuristic of Genetic algorithm based on SCX. The average percentage of error to get the best Known solution in our proposed work with the results obtained by applying a simple genetic algorithm using sequential constructive crossover for the quadratic assignment problem. The results show the effectiveness of the proposed CSO-QAP to solve the Quadratic assignment problem in term of time and quality of solutions. The proposed adaptation can be further applied by using a local search strategy to solve the same problem or another combinatorial problem.</p>

Universal strategy of dynamic subpopulation number management in practical network optimization problems

Recently, an idea of evolutionary multi-tasking has been proposed and applied to various types of optimization problems. The basic idea of evolutionary multi-tasking is to simultaneously solve multiple optimization problems (i.e., tasks) in a cooperative manner by a single run of an evolutionary algorithm. For this purpose, each individual in a population has its own task. This means that a population of individuals can be viewed as being divided into multiple sub-populations. The number of sub-populations is the same as the number of tasks to be solved. In this paper, first we explain that a multi-factorial evolutionary algorithm (MFEA), which is a representative algorithm of evolutionary multi-tasking, can be viewed as a special island model. MFEA has the following two features: (i) Crossover is performed not only within an island but also between islands, and (ii) no migration is performed between islands. Information of individuals in one island is transferred to another island through inter-island crossover. Next, we propose a simple implementation of evolutionary multi-tasking in the framework of the standard island model. Then, we compare our island model with MFEA through computational experiments. Promising results are obtained by our implementation of evolutionary multi-tasking.