Mining High Utility Itemsets with Hill Climbing and Simulated Annealing

High utility itemset mining (HUIM) is the task of finding all items set, purchased together, that generate a high profit in a transaction database. In the past, several algorithms have been developed to mine high utility itemsets (HUIs). However, most of them cannot properly handle the exponential search space while finding HUIs when the size of the database and total number of items increases. Recently, evolutionary and heuristic algorithms were designed to mine HUIs, which provided considerable performance improvement. However, they can still have a long runtime and some may miss many HUIs. To address this problem, this article proposes two algorithms for HUIM based on Hill Climbing (HUIM-HC) and Simulated Annealing (HUIM-SA). Both algorithms transform the input database into a bitmap for efficient utility computation and for search space pruning. To improve population diversity, HUIs discovered by evolution are used as target values for the next population instead of keeping the current optimal values in the next population. Through experiments on real-life datasets, it was found that the proposed algorithms are faster than state-of-the-art heuristic and evolutionary HUIM algorithms, that HUIM-SA discovers similar HUIs, and that HUIM-SA evolves linearly with the number of iterations.

Online Relaxation Refinement for Satisficing Planning: On Partial Delete Relaxation, Complete Hill-Climbing, and Novelty Pruning

In classical AI planning, heuristic functions typically base their estimates on a relaxation of the input task. Such relaxations can be more or less precise, and many heuristic functions have a refinement procedure that can be iteratively applied until the desired degree of precision is reached. Traditionally, such refinement is performed offline to instantiate the heuristic for the search. However, a natural idea is to perform such refinement online instead, in situations where the heuristic is not sufficiently accurate. We introduce several online-refinement search algorithms, based on hill-climbing and greedy best-first search. Our hill-climbing algorithms perform a bounded lookahead, proceeding to a state with lower heuristic value than the root state of the lookahead if such a state exists, or refining the heuristic otherwise to remove such a local minimum from the search space surface. These algorithms are complete if the refinement procedure satisfies a suitable convergence property. We transfer the idea of bounded lookaheads to greedy best-first search with a lightweight lookahead after each expansion, serving both as a method to boost search progress and to detect when the heuristic is inaccurate, identifying an opportunity for online refinement. We evaluate our algorithms with the partial delete relaxation heuristic hCFF, which can be refined by treating additional conjunctions of facts as atomic, and whose refinement operation satisfies the convergence property required for completeness. On both the IPC domains as well as on the recently published Autoscale benchmarks, our online-refinement search algorithms significantly beat state-of-the-art satisficing planners, and are competitive even with complex portfolios.

Penyelesaian Urban Transit Routing Problem Menggunakan Algoritma Hyper-heuristics berbasis Modified Particle Swarm Optimization based on Gravitational field Interactions

Kemacetan lalu lintas dapat diatasi dengan adanya public transport. Penerapan public transport yang optimal perlu dilakukan penentuan rute yang baik. Untuk mendapatkan rute public transport yang optimal, maka perlu dilakukan beberapa percobaan kombinasi antara jarak titik awal dan tujuan. Sehingga masalah dapat dikatakan sebagai masalah kombinatorik. VRP merupakan permasalahan kombinatorik. Oleh karena itu permasalahan dapat diselesaikan menggunakan metode metaheuristik. Penelitian ini akan menggunakan algoritma Modified Particle Swarm Optimization (MPSO-GI) dengan pendekatan Hyper-heuristics untuk menyelesaikan masalah penentuan rute public transport. Data yang digunakan merupakan dataset Mumford dan Mandl yang digunakan pada beberapa penelitian sebelumnya. Penelitian dilakukan dengan membandingkan hasil solusi yang dihasilkan oleh metode yang ditawarkan dengan hasil pada penelitian sebelumnya. Sehingga dapat diketahui kelebihan dan kekurangan dari metode yang ditawarkan. Berdasarkan hasil uji coba dapat ketahui bahwa algoritma MPSO-GI dengan pendekatan Hyper-Heuristics dapat diimpelmentasikan dan menyelesaikan masalah UTRP. MPSO-GI dengan pendekatan Hyper-Heuristics berhasil memperbaiki solusi hill-climbing di hamper semua dataset dengan nilai yang stabil. Hasil metode MPSO-GI dengan pendekatan Hyper-Heuristics unggul dalam menghasilkan solusi biaya penumpang pada dataset Mandl4, Mandl6, Mandl7, Mandl8 dan biaya operator pada dataset Mandl4 dan Mandl6 jika dibandingkan dengan metode pada penelitian sebelumnya.

Pembentukan Solusi Awal International Timetabling Competition 2021

Penjadawalan olahraga merupakan salah satu cabang dari optimasi di riset operasi. Penjadwalan olahraga memiliki berbagai macam batasan yang menantang para peneliti untuk menyelesaikannya. International Timetabling Competition (ITC) 2021 merupakan salah satu kompetisi optimasi yang menyediakan permasalahan penjadwalan olahraga. Permasalahan utama pada ITC 2021 yaitu menentukan jadwal waktu yang tepat untuk sebuah pertandingan. Sebuah jadwal dikatakan dapat digunakan (feasible) apabila tidak melanggar hard constraint yang ada. Pembentukan solusi awal yang feasible saat ini dapat dilakukan dengan algoritma constraint programming atau integer programming. Akan tetapi, kedua algoritma tersebut cukup rumit untuk diimplementasikan. Penelitian ini berfokus pada pembentukan solusi awal yang feasible dengan cara yang mudah untuk diimplementasikan. Cara yang digunakan yaitu dengan mengoptimasi pelanggaran hard constraint menggunakan algoritma Late Acceptance Hill Climbing (LAHC) dan Tabu Search dengan kerangka kerja Hyper-Heuristic yang melibatkan low level heuristic (LLH). Algoritma dijalankan maksimal dengan batasan waktu 6 jam untuk setiap data. Hasil dari optimasi pelanggaran hard constraint menggunakan algoritma LAHC dan tabu search dapat menghasilkan solusi awal yang feasible sebanyak 44.44% atau 24 dari 54 keseluruhan dataset.

A Novel Photovoltaic Module Quick Regulate MPPT Algorithm for Uniform Irradiation and Partial Shading Conditions

This study proposed a new photovoltaic module quick regulate (PVM-QR) maximum power point tracking (MPPT) algorithm, which can eliminate the disturbance problem of the hill-climbing (HC) algorithm, especially under low irradiance level and partial shading conditions (PSC). This proposed algorithm has the advantage that it only uses the detection photovoltaic module (PVM) impedance and the open-circuit voltage to simply and quickly calculate the PVM temperature, the irradiance level, and then the key factor parameter u. To achieve the MPPT quickly and accurately, this proposed algorithm is developed for the prediction of the best MPPT duty cycle based on the irradiance level, parameter u, and load. This study verified the proposed MPPT by the measurement results, where the HC algorithm MPPT has 95% efficiency at 0.55 kW/m2 but diverges at 0.2 kW/m2 under uniform irradiation conditions (UIC), and the proposed MPPT algorithm has an improved efficiency (99%) under the same conditions. Moreover, the proposed MPPT algorithm has 99% efficiency under PSC, while the HC algorithm MPPT’s efficiency is 66%. This study implemented a simple-design circuit with the proposed MPPT algorithm for UIC and PSC, where the actual experiment results can also verify that the proposed algorithm performs better than the HC algorithm.

Multi-Objective Optimisation of the Benchmark Wind Farm Layout Problem

Wind farm layout optimisation has become a very challenging and widespread problem in recent years. In many publications, the main goal is to achieve the maximum power output and minimum wind farm cost. This may be accomplished by applying single or multi-objective optimisation techniques. In this paper, we apply a single objective hill-climbing algorithm (HCA) and three multi-objective evolutionary algorithms (NSGA-II, SPEA2 and PESA-II) to a well-known benchmark optimisation problem proposed by Mosetti et al., which includes three different wind scenarios. We achieved better results by applying single- and multi-objective algorithms. Furthermore, we showed that the best performing multi-objective algorithm was NSGA-II. Finally, an extensive comparison of the results of past publications is made.

Artificial Intelegence PENERAPAN KASUS ALGORITMA ASCENT HILL CLIMBING DALAM PERMAINAN PUZZLE 8

Puzzle game is a game that shifts numbers from a box consisting of nine boxes. Eight boxes must have values arranged in numerical order starting from numbers 1 to 8. Puzzle games can produce the correct sequence according to the initial state provided that they follow the rules established rules. Completion of this game using a heuristic method, using the Ascent hill Climbing algorithm. The working process of the Ascent hill Climbing method is a process of looking for several possible solutions in order to get the optimal value for solving the problem by arranging the values from the position of the smallest value to the position of the largest value. The problem that is often experienced in this case is a lack of user knowledge in the concept of puzzle game rules so that search results are difficult to find,with this method it can make it easier to solve puzzle game cases by following the game rules and done systematically so that Goals are quickly found. The Goal results obtained are in the form of steps in the process of finding a solution and calculating the time required in the search to find a solution.

Artificial Intelegence Pemanfaatan Metode Hill Climbing Mencari Lintasan Terpendek Objek Wisata Menggunakan sistem Informasi

The search is often used to search for the shortest route, the Hill Climbing Method is a part of the test that uses heuristic functions. The problem that is often encountered is in the form of miscalculations in calculating the distance so that it requires long distances, costs a lot and takes a very long time. To solve this case, it can be solved by making a structure graph by looking at the city points from the two sides of the point to be passed. Using an algorithm can help make it easier to find a location and save time and travel costs that will be passed. This advantage is that all points will be obtained and checked from the right and left sides one by one so as to obtain effective and maximum results. The Hill Climbing method that will be used has the concept of a geographic information system as a guide and is used as a system for decision making. The heuristic search method is one of the methods commonly used in finding a way

One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms

This paper addresses the problem of scheduling a set of jobs on a machine with time-varying capacity, with the goal of minimizing the total tardiness objective function. This problem arose in the context scheduling the charging times of a fleet of electric vehicles and it is NP-hard. Recent work proposed an efficient memetic algorithm for solving the problem, combining a genetic algorithm and a local search method. The local search procedure is based on swapping consecutive jobs on a C-path, defined as a sequence of consecutive jobs in a schedule. Building on it, this paper develops new memetic algorithms that stem from new local search procedures also proposed in this paper. The local search methods integrate several mechanisms to make them more effective, including a new condition for swapping pairs of jobs, a hill climbing approach, a procedure that operates on several C-paths and a method that interchanges jobs between different C-paths. As a result, the new local search methods enable the memetic algorithms to reach higher-quality solutions. Experimental results show significant improvements over existing approaches.