Land-Use Planning for Farming Area in West Java to Divide Allocation of Vegetables Commodity Using Genetic Algorithm Approach

This research has created a model to determine the optimum allocation of land-use planning for farming in West Java by considering the two main components, i.e., production and cost. The method is essential in farming, especially in the COVID-19 situation, as it determines clearly which procedure needs to be involved for land-use farming optimization. The problem of land allocation lies in finding the optimum solution from the multi-objective functions. In this study, the method used to cope with the land-use design problem was the Genetic Algorithm (GA) and its expansion called Nondominated Sorting Genetic Algorithm (NSGA). The research results indicated that the best total fitness in GA and NSGA is relatively the same. It was shown that both NSGA and GA could make a planning scheme optimal for the farming commodities in West Java. Based on the maximum optimum value from the best fitness value of NSGA, around 37.35% of the farmland in West Java, it is the best fit for the big red chili commodity. The city where the land used for extensive red chili farming is found to have the maximum optimum value is Garut, with 98.73% of its total farm area.This research has created a model to determine the optimum allocation of land-use planning for farming in West Java by considering the two main components, i.e., production and cost. The method is essential in farming, especially in the COVID-19 situation, as it determines clearly which procedure needs to be involved for land-use farming optimization. The problem of land allocation lies in finding the optimum solution from the multi-objective functions. In this study, the method used to cope with the land-use design problem was the Genetic Algorithm (GA) and its expansion called Nondominated Sorting Genetic Algorithm (NSGA). The research results indicated that the best total fitness in GA and NSGA is relatively the same. It was shown that both NSGA and GA could make a planning scheme optimal for the farming commodities in West Java. Based on the maximum optimum value from the best fitness value of NSGA, around 37.35% of the farmland in West Java, it is the best fit for the big red chili commodity. The city where the land used for extensive red chili farming is found to have the maximum optimum value is Garut, with 98.73% of its total farm area

A comparison study on meta-heuristics for ground station scheduling problem

In ground station scheduling problem the aim is to compute an optimal planning of communications between Spacecrafts (SCs) and operations teams of Ground Stations (GSs). While such allocation of tasks to ground stations traditionally is mostly done by human intervention, modern scheduling systems look at optimization and automation features. Such features, on the one hand, would increase the efficiency and productivity of the mission planning systems by handling a larger number of missions, achieve a higher usage of the infrastructure (grand stations' antennae) and, on the other, would avoid error-prone human allocation and reduce human labour costs. Designing such modern, automated scheduling/planning systems is however challenging due to the highly constraint and complex nature of the problem seeking to optimize along various objectives or system parameters. In this paper we present a study on the performance of several meta-heuristics methods for solving ground station scheduling problem. Local search methods (Hill Climbing, Simulated Annealing and Tabu Search) and population-based methods (GA, Steady State GA and Struggle GA) have been considered for the study. The performance of these resolution methods was measured by a set of instances of varying size and complexity generated by STK toolkit. The study revealed the strengths and weaknesses of the considered methods while solving different size instances and considering several objective functions, namely, windows fitness, clashes fitness, time requirement fitness, and resource usage fitness.Peer ReviewedPostprint (author's final draft

Greedy algorithms for sensor location in sewer systems

Wastewater quality monitoring is receiving growing interest with the necessity of developing new strategies for controlling accidental and intentional illicit intrusions. In designing a monitoring network, a crucial aspect is represented by the sensors’ location. In this study, a methodology for the optimal placement of wastewater monitoring sensors in sewer systems is presented. The sensor location is formulated as an optimization problem solved using greedy algorithms (GRs). The StormWater Management Model (SWMM) was used to perform hydraulic and water-quality simulations. Six different procedures characterized by different fitness functions are presented and compared. The performances of the procedures are tested on a real sewer system, demonstrating the suitability of GRs for the sensor-placement problem. The results show a robustness of the methodology with respect to the detection concentration parameter, and they suggest that procedures with multiple objectives into a single fitness function give better results. A further comparison is performed using previously developed multi-objective procedures with multiple fitness functions solved using a genetic algorithm (GA), indicating better performances of the GR. The existing monitoring network, realized without the application of any sensor design, is always suboptimal

Monotonicity of Fitness Landscapes and Mutation Rate Control

A common view in evolutionary biology is that mutation rates are minimised. However, studies in combinatorial optimisation and search have shown a clear advantage of using variable mutation rates as a control parameter to optimise the performance of evolutionary algorithms. Much biological theory in this area is based on Ronald Fisher's work, who used Euclidean geometry to study the relation between mutation size and expected fitness of the offspring in infinite phenotypic spaces. Here we reconsider this theory based on the alternative geometry of discrete and finite spaces of DNA sequences. First, we consider the geometric case of fitness being isomorphic to distance from an optimum, and show how problems of optimal mutation rate control can be solved exactly or approximately depending on additional constraints of the problem. Then we consider the general case of fitness communicating only partial information about the distance. We define weak monotonicity of fitness landscapes and prove that this property holds in all landscapes that are continuous and open at the optimum. This theoretical result motivates our hypothesis that optimal mutation rate functions in such landscapes will increase when fitness decreases in some neighbourhood of an optimum, resembling the control functions derived in the geometric case. We test this hypothesis experimentally by analysing approximately optimal mutation rate control functions in 115 complete landscapes of binding scores between DNA sequences and transcription factors. Our findings support the hypothesis and find that the increase of mutation rate is more rapid in landscapes that are less monotonic (more rugged). We discuss the relevance of these findings to living organisms

Multiple Objective Fitness Functions for Cognitive Radio Adaptation

This thesis explores genetic algorithm and rule-based optimization techniques used by cognitive radios to make operating parameter decisions. Cognitive radios take advantage of intelligent control methods by using sensed information to determine the optimal set of transmission parameters for a given situation. We have chosen to explore and compare two control methods. A biologically-inspired genetic algorithm (GA) and a rule-based expert system are proposed, analyzed and tested using simulations. We define a common set of eight transmission parameters and six environment parameters used by cognitive radios, and develop a set of preliminary fitness functions that encompass the relationships between a small set of these input and output parameters. Five primary communication objectives are also defined and used in conjunction with the fitness functions to direct the cognitive radio to a solution. These fitness functions are used to implement the two cognitive control methods selected. The hardware resources needed to practically implement each technique are studied. It is observed, through simulations, that several trade offs exist between both the accuracy and speed of the final decision and the size of the parameter sets used to determine the decision. Sensitivity analysis is done on each parameter in order to determine the impact on the decision making process each parameter has on the cognitive engine. This analysis quantifies the usefulness of each parameter