A COMPARATIVE STUDY OF HEURISTIC OPTIMIZATION ALGORITHMS

Heuristic optimization algorithms are of great importance for reaching solutions to various real world problems. These algorithms have a wide range of applications such as cost reduction, artificial intelligence, and medicine. By the term cost, one could imply that that cost is associated with, for instance, the value of a function of several independent variables. Often, when dealing with engineering problems, we want to minimize the value of a function in order to achieve an optimum, or to maximize another parameter which increases with a decrease in the cost (the value of this function). The heuristic cost reduction algorithms work by finding the optimum values of the independent variables for which the value of the function (the “cost”) is the minimum. There is an abundance of heuristic cost reduction algorithms to choose from. We will start with a discussion of various optimization algorithms such as Memetic algorithms, force-directed placement, and evolution-based algorithms. Following this initial discussion, we will take up the working of three algorithms and implement the same in MATLAB. The focus of this report is to provide detailed information on the working of three different heuristic optimization algorithms, and conclude with a comparative study on the performance of these algorithms when implemented in MATLAB. In this report, the three algorithms we will take in to consideration will be the non-adaptive simulated annealing algorithm, the adaptive simulated annealing algorithm, and random restart hill climbing algorithm. The algorithms are heuristic in nature, that is, the solution these achieve may not be the best of all the solutions but provide a means to reach a quick solution that may be a reasonably good solution without taking an indefinite time to implement

Implementasi Algoritma Integer Linear Programming Untuk Sistem Informasi Penjadwalan Ruangan Di Fakultas Ilmu Komputer Universitas Indonesia

Permasalahan konflik penjadwalan ruangan (timetabling) sering dihadapi hampir sebagian besar institusi akademis di Indonesia, salah satunya di Fakultas Ilmu Komputer Universitas Indonesia (Fasilkom UI). Peningkatan jumlah mahasiswa setiap tahun yang tidak diikuti oleh peningkatan jumlah dan kapasitas kelas menjadi faktor penyebab utama. Selama ini sistem penjadwalan masih dilakukan secara manual, sehingga membutuhkan waktu yang relatif lama dan menyebabkan optimasi pengalokasian kebutuhan ruangan menjadi kurang efisien. Penelitian ini bertujuan untuk menemukan pendekatan yang sesuai dalam menyelesaikan masalah timetabling tersebut. Beberapa pendekatan yang dapat digunakan untuk menyelesaikan masalah ini antara lain algoritma Tabu Search, Simmulated Annealing, Graph Coloring, dan Integer Linear Programming (ILP). Dalam penelitian ini, peneliti menggunakan algoritma ILP karena ILP merupakan model yang paling tepat untuk menyelesaikan masalah timetabling di Fasilkom UI. Algoritma ini dapat meminimalkan waktu yang diperlukan untuk melakukan penjadwalan dari sebulan menjadi hitungan menit. Room scheduling conflict issues (timetabling) are facing most of the academic institutions in Indonesia, one is in the Faculty of Computer Science (Fasilkom) Universitas Indonesia (UI). In the number of students each year followed by no increase in the number and capacity of the class became the main factor. During this scheduling system is still done manually so it takes a relatively long time so that the optimization is less efficient allocation of space requirements. This study aims to find an appropriate approach in solving the timetabling problem. Several approaches can be used to solve these problems include Tabu Search algorithm, Simmulated Annealing, Graph Coloring, and Integer Linear Programming (ILP). In this study we used the ILP algorithm for ILP is the most appropriate model to solve the timetabling problem in Fasilkom UI. This algorithm can minimize the time required to perform the scheduling of a month becomes a matter of minutes

IMPLEMENTASI ALGORITMA INTEGER LINEAR PROGRAMMING UNTUK SISTEM INFORMASI PENJADWALAN RUANGAN DI FAKULTAS ILMU KOMPUTER UNIVERSITAS INDONESIA

Permasalahan konflik penjadwalan ruangan (timetabling) sering dihadapi hampir sebagian besar institusi akademis di Indonesia, salah satunya di Fakultas Ilmu Komputer Universitas Indonesia (Fasilkom UI). Peningkatan jumlah mahasiswa setiap tahun yang tidak diikuti oleh peningkatan jumlah dan kapasitas kelas menjadi faktor penyebab utama. Selama ini sistem penjadwalan masih dilakukan secara manual, sehingga membutuhkan waktu yang relatif lama dan menyebabkan optimasi pengalokasian kebutuhan ruangan menjadi kurang efisien. Penelitian ini bertujuan untuk menemukan pendekatan yang sesuai dalam menyelesaikan masalah timetabling tersebut. Beberapa pendekatan yang dapat digunakan untuk menyelesaikan masalah ini antara lain algoritma Tabu Search, Simmulated Annealing, Graph Coloring, dan Integer Linear Programming (ILP). Dalam penelitian ini, peneliti menggunakan algoritma ILP karena ILP merupakan model yang paling tepat untuk menyelesaikan masalah timetabling di Fasilkom UI. Algoritma ini dapat meminimalkan waktu yang diperlukan untuk melakukan penjadwalan dari sebulan menjadi hitungan menit. Room scheduling conflict issues (timetabling) are facing most of the academic institutions in Indonesia, one is in the Faculty of Computer Science (Fasilkom) Universitas Indonesia (UI). In the number of students each year followed by no increase in the number and capacity of the class became the main factor. During this scheduling system is still done manually so it takes a relatively long time so that the optimization is less efficient allocation of space requirements. This study aims to find an appropriate approach in solving the timetabling problem. Several approaches can be used to solve these problems include Tabu Search algorithm, Simmulated Annealing, Graph Coloring, and Integer Linear Programming (ILP). In this study we used the ILP algorithm for ILP is the most appropriate model to solve the timetabling problem in Fasilkom UI. This algorithm can minimize the time required to perform the scheduling of a month becomes a matter of minutes

Scaling Nonparametric Bayesian Inference via Subsample-Annealing

We describe an adaptation of the simulated annealing algorithm to nonparametric clustering and related probabilistic models. This new algorithm learns nonparametric latent structure over a growing and constantly churning subsample of training data, where the portion of data subsampled can be interpreted as the inverse temperature beta(t) in an annealing schedule. Gibbs sampling at high temperature (i.e., with a very small subsample) can more quickly explore sketches of the final latent state by (a) making longer jumps around latent space (as in block Gibbs) and (b) lowering energy barriers (as in simulated annealing). We prove subsample annealing speeds up mixing time N^2 -> N in a simple clustering model and exp(N) -> N in another class of models, where N is data size. Empirically subsample-annealing outperforms naive Gibbs sampling in accuracy-per-wallclock time, and can scale to larger datasets and deeper hierarchical models. We demonstrate improved inference on million-row subsamples of US Census data and network log data and a 307-row hospital rating dataset, using a Pitman-Yor generalization of the Cross Categorization model.Comment: To appear in AISTATS 201

Quantum Optimization of Fully-Connected Spin Glasses

The Sherrington-Kirkpatrick model with random $\pm1$ couplings is programmed on the D-Wave Two annealer featuring 509 qubits interacting on a Chimera-type graph. The performance of the optimizer compares and correlates to simulated annealing. When considering the effect of the static noise, which degrades the performance of the annealer, one can estimate an improvement on the comparative scaling of the two methods in favor of the D-Wave machine. The optimal choice of parameters of the embedding on the Chimera graph is shown to be associated to the emergence of the spin-glass critical temperature of the embedded problem.Comment: includes supplemental materia

Intelligence Surveillance and Reconnaissance Asset Assignment for Optimal Mission Effectiveness

This research develops mathematical programming techniques to solve an intelligence, surveillance, and reconnaissance sensor assignment problem for USSTRATCOM. The problem as specified is hypothesized to be difficult (i.e. np-hard). With the smallest test cases, the true optimal solution is found using simple optimization techniques, but, due to intractability, the optimal solutions for larger test cases are not found using these same techniques. Instead, heuristic techniques are applied to several test cases in order to determine the best, robust methodologies to find true or near optimal solutions. Specifically, simulated annealing (SA) is tested for convergence properties across several different parameter settings. This research also utilizes local search techniques with simple exchange neighborhoods of various sizes. Mission prioritization is also examined via a weighted sum scalarization technique

Faster Convex Optimization: Simulated Annealing with an Efficient Universal Barrier

This paper explores a surprising equivalence between two seemingly-distinct convex optimization methods. We show that simulated annealing, a well-studied random walk algorithms, is directly equivalent, in a certain sense, to the central path interior point algorithm for the the entropic universal barrier function. This connection exhibits several benefits. First, we are able improve the state of the art time complexity for convex optimization under the membership oracle model. We improve the analysis of the randomized algorithm of Kalai and Vempala by utilizing tools developed by Nesterov and Nemirovskii that underly the central path following interior point algorithm. We are able to tighten the temperature schedule for simulated annealing which gives an improved running time, reducing by square root of the dimension in certain instances. Second, we get an efficient randomized interior point method with an efficiently computable universal barrier for any convex set described by a membership oracle. Previously, efficiently computable barriers were known only for particular convex sets