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.

Digital Image Evolution of Artwork Without Human Evaluation Using the Example of the Evolving Mona Lisa Problem

Whether for optimizing the speed of microprocessors or for sequence analysis in molecular biology — evolutionary algorithms are used in astoundingly many fields. Also, the art was influenced by evolutionary algorithms — with principles of natural evolution works of art that can be created or imitated, whereby initially generated art is put through an iterated process of selection and modification. This paper covers an application in which given images are emulated evolutionary using a finite number of semi-transparent overlapping polygons, which also became known under the name “Evolution of Mona Lisa”. In this context, different approaches to solve the problem are tested and presented here. In particular, we want to investigate whether Hill Climbing Algorithm in combination with Delaunay Triangulation and Canny Edge Detector that extracts the initial population directly from the original image performs better than the conventional Hill Climbing and Genetic Algorithm, where the initial population is generated randomly.

PERANCANGAN ZETA CONVERTER PADA PELACAKAN TITIK DAYA MAKSIMUM PHOTOVOLTAIC MENGGUNAKAN ALGORITMA MODIFIKASI HILL CLIMBING

Electrical energy is a primary need at this time, which almost all human activities require electricity. The electrical energy we use today is a conversion from other energy, partly derived from fossil energy, which is energy that cannot be renewed and will run out if it is continuously explored and exploited. Solar energy is a renewable energy source that has the potential to be converted to electrical energy using solar panels or so-called photovoltaics. Photovoltaic has a drawback in its use, namely the output value is very dependent on environmental conditions. To maximize the power efficiency between the photovoltaic output and the power to be used by the load, a method is needed, namely Maximum Power Point Tracking (MPPT). In the application of this MPPT DC-DC Zeta converter is used with a hill climbing algorithm to achieve the value of the output voltage and current at maximum power. With this method, it is expected that MPPT control is reliable and easy to apply. In this study, the type of photovoltaic module used is the 60 Wp monocrystalline type with sampling data once an hour from 09.00 - 17.00 WITA, the tracking speed data obtained by the modified hill climbing algorithm is 0.142 seconds on average with an average efficiency of 99.969 %.

High Speed Hill Climbing Algorithm for Portfolio Optimization

Portfolio can be defined as a collection of investments. Portfolio optimization usually is about maximizing expected return and/or minimising risk of a portfolio. The mean-variance model makes simplifying assumptions to solve portfolio optimization problem. Presence of realistic constraints leads to a significant different and complex problem. Also, the optimal solution under realistic constraints cannot always be derived from the solution for the frictionless market. The heuristic algorithms are alternative approaches to solve the extended problem. In this research, a heuristic algorithm is presented and improved for higher efficiency and speed. It is a hill climbing algorithm to tackle the extended portfolio optimization problem. The improved algorithm is Hill Climbing Simple–with Reducing Thresh-hold Percentage, named HC-S-R. It is applied in standard portfolio optimization problem and benchmarked with the quadratic programing method and the Threshold Accepting algorithm, a well-known heuristic algorithm for portfolio optimization problem. The results are also compared with its original algorithm HC-S. HC-S-R proves to be a lot faster than HC-S and TA and more effective and efficient than TA. Keywords: Portfolio optimization; Hill climbing algorithm; Threshold percentage; Reducing sequence; Threshold Acceptance algorithm

NIgPred: Class-Specific Antibody Prediction for Linear B-Cell Epitopes Based on Heterogeneous Features and Machine-Learning Approaches

Upon invasion by foreign pathogens, specific antibodies can identify specific foreign antigens and disable them. As a result of this ability, antibodies can help with vaccine production and food allergen detection in patients. Many studies have focused on predicting linear B-cell epitopes, but only two prediction tools are currently available to predict the sub-type of an epitope. NIgPred was developed as a prediction tool for IgA, IgE, and IgG. NIgPred integrates various heterologous features with machine-learning approaches. Differently from previous studies, our study considered peptide-characteristic correlation and autocorrelation features. Sixty kinds of classifier were applied to construct the best prediction model. Furthermore, the genetic algorithm and hill-climbing algorithm were used to select the most suitable features for improving the accuracy and reducing the time complexity of the training model. NIgPred was found to be superior to the currently available tools for predicting IgE epitopes and IgG epitopes on independent test sets. Moreover, NIgPred achieved a prediction accuracy of 100% for the IgG epitopes of a coronavirus data set. NIgPred is publicly available at our website.