Incorporating Memory and Learning Mechanisms Into Meta-RaPS

Due to the rapid increase of dimensions and complexity of real life problems, it has become more difficult to find optimal solutions using only exact mathematical methods. The need to find near-optimal solutions in an acceptable amount of time is a challenge when developing more sophisticated approaches. A proper answer to this challenge can be through the implementation of metaheuristic approaches. However, a more powerful answer might be reached by incorporating intelligence into metaheuristics. Meta-RaPS (Metaheuristic for Randomized Priority Search) is a metaheuristic that creates high quality solutions for discrete optimization problems. It is proposed that incorporating memory and learning mechanisms into Meta-RaPS, which is currently classified as a memoryless metaheuristic, can help the algorithm produce higher quality results. The proposed Meta-RaPS versions were created by taking different perspectives of learning. The first approach taken is Estimation of Distribution Algorithms (EDA), a stochastic learning technique that creates a probability distribution for each decision variable to generate new solutions. The second Meta-RaPS version was developed by utilizing a machine learning algorithm, Q Learning, which has been successfully applied to optimization problems whose output is a sequence of actions. In the third Meta-RaPS version, Path Relinking (PR) was implemented as a post-optimization method in which the new algorithm learns the good attributes by memorizing best solutions, and follows them to reach better solutions. The fourth proposed version of Meta-RaPS presented another form of learning with its ability to adaptively tune parameters. The efficiency of these approaches motivated us to redesign Meta-RaPS by removing the improvement phase and adding a more sophisticated Path Relinking method. The new Meta-RaPS could solve even the largest problems in much less time while keeping up the quality of its solutions. To evaluate their performance, all introduced versions were tested using the 0-1 Multidimensional Knapsack Problem (MKP). After comparing the proposed algorithms, Meta-RaPS PR and Meta-RaPS Q Learning appeared to be the algorithms with the best and worst performance, respectively. On the other hand, they could all show superior performance than other approaches to the 0-1 MKP in the literature

Meta-RaPS Algorithm for the Aerial Refueling Scheduling Problem

The Aerial Refueling Scheduling Problem (ARSP) can be defined as determining the refueling completion times for each fighter aircraft (job) on multiple tankers (machines). ARSP assumes that jobs have different release times and due dates, The total weighted tardiness is used to evaluate schedule's quality. Therefore, ARSP can be modeled as a parallel machine scheduling with release limes and due dates to minimize the total weighted tardiness. Since ARSP is NP-hard, it will be more appropriate to develop a ppro~imate or heuristic algorithm to obtain solutions in reasonable computation limes. In this paper, Meta-Raps-ATC algorithm is implemented to create high quality solutions. Meta-RaPS (Meta-heuristic for Randomized Priority Search) is a recent and promising meta heuristic that is applied by introducing randomness to a construction heuristic. The Apparent Tardiness Rule (ATC), which is a good rule for scheduling problems with tardiness objective, is used to construct initial solutions which are improved by an exchanging operation. Results are presented for generated instances

Tuning Parameters in Heuristics by Using Design of Experiments Methods

With the growing complexity of today's large scale problems, it has become more difficult to find optimal solutions by using exact mathematical methods. The need to find near-optimal solutions in an acceptable time frame requires heuristic approaches. In many cases, however, most heuristics have several parameters that need to be "tuned" before they can reach good results. The problem then turns into "finding best parameter setting" for the heuristics to solve the problems efficiently and timely. One-Factor-At-a-Time (OFAT) approach for parameter tuning neglects the interactions between parameters. Design of Experiments (DOE) tools can be instead employed to tune the parameters more effectively. In this paper, we seek the best parameter setting for a Genetic Algorithm (GA) to solve the single machine total weighted tardiness problem in which n jobs must be scheduled on a single machine without preemption, and the objective is to minimize the total weighted tardiness. Benchmark instances for the problem are available in the literature. To fine tune the GA parameters in the most efficient way, we compare multiple DOE models including 2-level (2k ) full factorial design, orthogonal array design, central composite design, D-optimal design and signal-to-noise (SIN) ratios. In each DOE method, a mathematical model is created using regression analysis, and solved to obtain the best parameter setting. After verification runs using the tuned parameter setting, the preliminary results for optimal solutions of multiple instances were found efficiently

Commercial Regional Space/Airborne Imaging

In this work goal programming is used to solve a minimum cost multicommodity network flow problem with multiple goals. A single telecommunication network with multiple commodities (e.g., voice, video, data, etc.) flowing over it is analyzed. This network consists of: linear objective function, linear cost arcs, fixed capacities, specific origin-destination pairs for each commodity. A multicommodity network flow problem with goals can be successfully modeled using linear goal programming techniques. When properly modeled, network flow techniques may be employed to exploit the pure network structure of a multicommodity network flow problem with goals. Lagrangian relaxation captures the essence of the pure network flow problem as a master problem and sub-problems (McGinnis and Rao, 1977). A subgradient algorithm may optimize the Lagrangian function, or the Lagrangian relaxation could be decomposed into subproblems per commodity; each subproblem being a single commodity network flow problem. Parallel to the decomposition of the Lagrangian relaxation, Dantzig-Wolfe decomposition may be implemented to the linear program. Post-optimality analyses provide a variety of options to analyze the robustness of the optimal solution. The options of post-optimality analysis consist of sensitivity analysis and parametric analysis. This mix of modeling options and analyses provide a powerful method to produce insight into the modeling of a multicommodity network flow problem with multiple objectives

Piagam Debest: Integrasi Komitmen Tripusat Pendidikan Untuk Penguatan Pendidikan Karakter di SD Muhammadiyah 24 Surabaya

Tantangan pendidikan di era millenial menjadi sangat kompleks. Kemajuan teknologi, gempuran budaya asing serta erosi kearifan lokal sebagai karakter bangsa Indonesia perlu direspon dengan baik. Untuk itu diperlukan kolaborasi dari berbagai komponen pendidikan dalam menghadapi kompleksitas tantangan masa depan pendidikan tersebut. Sebagai tripusat pendidikan, peranan keluarga, sekolah dan masyarakat harus mampu dikomunikasikan dengan baik agar dapat menjadi perisai dalam membentengi generasi muda dalam menghadapi gempuran budaya yang tidak sesuai dengan karakter bangsa. Pola komunikasi yang baik bagi ketiga komponen utama pendidikan tersebut dikonsep secara matang sehingga pola integrasi komitmen dalam penguatan pendidikan karakter dapat berjalan efektif. Tujuan penelitian ini adalah untuk mendeskripsikan integrasi komitmen tripusat pendidikan yang sudah diinisiasi dan dilaksanakan SD Muhammadiyah 24 Surabaya. Metode penelitian yang digunakan adalah penelitian lapangan, pendekatan kualitatif-studi kasus SD Muhammadiyah 24 Surabaya. Hasil penelitian bahwa melalui Piagam DE BEST sebagai bentuk integrasi komitmen tripusat pendidikan dalam menopang pendidikan karakter yang menjadi visi dari SD Muhammadiyah 24 Surabaya sudah berjalan efektif. Kata Kunci : TriPusat Pendidikan, Pendidikan Karakter, SD Muhammadiyah 24 Surabaya  

Hubungan Kecepatan,Kelincahan, dan Keterampilan Menggiring Bola pada Pemain Futsal

Background : An important component that must be mastered by a futsal player is discipline that is built with a high awareness for the creation of a good lifestyle and excellent physical condition, because the sport requires a lot of movement such as forward, backward, and survive, then the special technique one must have is dribbling skills that require physical skills that allow individuals to quickly and efficiently change direction, accelerate, and slow down in an effort to react appropriately. If a futsal player has good speed and agility while dribbling, then futsal players can free themselves from the opponent's escort and the ball is not easily taken by the opponent. Purpose : To know the relation of speed and agility of futsal players, speed relation with dribbling skills on futsal players, agility relationship with dribbling skills on futsal players, speed relationship, agility, and dribbling skills on futsal players. Method : This research is a type of observational analytic research with crossectional approach. Technical sampling using total sampling counted 50 respondents. Data analysis using pearson product moment. Result : Data obtained from normality test p> 0,05 then normal distribution. The result of correlation of velocity and agility shows that p <0,05 meaning that there is relation, and value r = 0,539 which means have strong and positive relation strength. The results of speed correlation and dribbling skills show that p <0.05, which means there is a relationship and value of r = 0.471 which means having the power of a moderate and positive relationship. The result of agility correlation and dribbling skill that p <0,05 with r value = 0,434 which means have medium strength and positive. Multiple correlation result mention p value <0,05 and value r = 0,517 hence degree degree relation expressed strong and positive correlation Conclusion : There is a relationship between speed and agility, there is a relation of speed with dribbling skills, there is an agility relationship with dribbling skills, there is a relationship between speed, agility, and dribbling skills on futsal players. Keywords : Speed, agility, dribbling

Pengaruh Balance Strategy Exercise dan Gaze Stability Exercise terhadap Peningkatan Keseimbangan pada Lansia

Balance is the ability to maintain and stabilize body position. the ability to maintain body position will decrease with age. to maintain the quality of life of the elderly a balance exercise is needed. there are various types of exercises that can be applied to the elderly, such as the balance strategy exercise and gaze stability exercise.The research objective to find out the differences in the effect of training between the balance strategy exercise and gaze stability exercise on balance in the elderly. The type of research conducted was experimental with a quasi experimental approach with pre and post test research design by comparing 2 groups. Respondents from this study were 20 people divided into two groups, group I a number of 10 people were given the balance strategy exercise and group II a number of 10 people were treated with gaze stability exercise. Measurement of balance in the elderly using Berg Balance Scale. The result using paired sample t-test in group I p = 0,000 (p <0.05) and group II p = 0.001 (p <0.05), this indicates that both treatments were given in group I and II has an influence on increasing balance in the elderly. While the results of the independent sample t-test in the treatment group p = 0302 (p <0.05), this shows that the treatment carried out in groups I and II did not have a difference in effect on increasing balance in the elderly. The conclusion of this study is that there is an effect of balance strategy exercise and gaze stability exercise in each group, but there is no significant difference in effect on balance training exercises and stability exercise views on improving balance in the elderly

Fundamental of Intrepreneurship (ENT300) : Athletes Rehabilitation (AR7) / Dayang Nurul Hanadira Elpis... [et al.]

AR7 is a partnership company that is generally known as rehabilitation service in Kuching and Kota Samarahan. AR7 will be providing best quality of service to our customers. We are ready to make our customers be satisfied with the services provided. Our company consists of seven members whereby all the members are giving their supports to the project. All the members contribute the same amount of capital. The reason we chose rehabilitation service because due to a high demand from customers especially athletes around Kuching and Kota Samarahan. Another reason it is because of its strategic location, other than it is located near to the main road

Local search versus Path Relinking in metaheuristics: Redesigning Meta-RaPS with application to the multidimensional knapsack problem

Most heuristics for discrete optimization problems consist of two phases: a greedy-based construction phase followed by an improvement (local search) phase. Although the best solutions are usually generated after the improvement phase, there is usually a high computational cost for employing a local search algorithm. This paper seeks another alternative to reduce the computational burden of a local search while keeping solution quality by embedding intelligence in metaheuristics. A modified version of Path Relinking is introduced to replace the local search in the improvement phase of Meta-RaPS (Meta Heuristic for Randomized Priority Search) which is currently classified as a memoryless metaheuristic. The new algorithm is tested using the 0-1 multidimensional knapsack problem, and it is observed that it could solve even the largest benchmark problems in significantly less time while maintaining solution quality compared to other algorithms in the literature

Integrating estimation of distribution algorithms versus Q-learning into Meta-RaPS for solving the 0-1 multidimensional knapsack problem

Finding near-optimal solutions in an acceptable amount of time is a challenge when developing sophisticated approximate approaches. A powerful answer to this challenge might be reached by incorporating intelligence into metaheuristics. We propose integrating two methods into Meta-RaPS (Metaheuristic for Randomized Priority Search), which is currently classified as a memoryless metaheuristic. The first method is the Estimation of Distribution Algorithms (EDA), and the second is utilizing a machine learning algorithm known as Q-Learning. To evaluate their performance, the proposed algorithms are tested on the 0-1 Multidimensional Knapsack Problem (MKP). Meta-RaPS EDA appears to perform better than Meta-RaPS Q-Learning. However, both showed promising results compared to other approaches presented in the literature for the 0-1 MKP