Model Penentuan Rute Terpendek Penjemputan Sampah Menggunakan Metode MTSP dan Algoritma Genetika

Garbage pick-ups performed by two or more people must have a route in their pickup. However, it is not easy to model the route of the pickup that each point must be passed and each point is only passed once. Now, the method to create a route has been done a lot, one of the most commonly used methods is the creation of routes using the Traveling Salesman Problem method. Traveling Salesman Problem is a method to determine the route of a series of cities where each city is only traversed once. In this study, the shortest route modeling was conducted using Multiple Traveling Salesman Problem and Genetic Algorithm to find out the shortest route model that can be passed in garbage pickup. In this study, datasets will be used as pick-up points to then be programmed to model the shortest routes that can be traveled. The application of Multiple Traveling Salesman Problem method using Genetic Algorithm shows success to model garbage pickup route based on existing dataset, by setting the parameters of 100 generations and 100 population and 4 salesmen obtained 90% of the best individual opportunities obtained with the best individual fitness value of 0.05209. The test was conducted using BlackBox testing and the results of this test that the functionality on the system is 100% appropriate

An Efficient Solution to Travelling Salesman Problem using Genetic Algorithm with Modified Crossover Operator

The traveling salesman problem (TSP) is a famous NP-hard problem in the area of combinatorial optimization. It is utilized to locate the shortest possible route that visits every city precisely once and comes back to the beginning point from a given set of cities and distance. This paper proposes an efficient and effective solution for solving such a query. A modified crossover method using Minimal Weight Variable, Order Selection Crossover operator, a modified mutation using local optimization and a modified selection method using KMST is proposed. The crossover operator (MWVOSX) chooses a particular order from multiple orders which have the minimum cost and takes the remaining from the other parent in backward and forward order. Then it creates two new offspring. Further, it selects the least weight new offspring from those two offspring. The efficiency of the proposed algorithm is compared to the classical genetic algorithm. Comparisons show that our proposed algorithm provides much efficient results than the existing classical genetic algorithm

Algoritma Genetika untuk Optimasi Komposisi Makanan Bagi Penderita Hipertensi

Hypertension can be prevented and handled by eating nutritious foods with the right composition. The genetic algorithm can be used to optimize the food composition for people with hypertension. Data used include sex, age, weight, height, activity type, stress level, and patient hypertension level. This study uses a reproduction method that is good enough to be applied to integer chromosome representations so that the search results provided are not local optimum solutions. The testing results show that the best genetic algorithm parameters are as follows population size is 15 with average fitness 20.97, the generation number is 40 with average fitness 50.10, and combination crossover rate and mutation rate are 0.3 and 0.7 with average fitness 41.67. The solution obtained is the optimal food composition for people with hypertension.Hipertensi dapat dicegah dan ditangani dengan mengonsumsi makanan bergizi dengan komposisi yang tepat. Algoritma genetika dapat digunakan untuk mengoptimalkan komposisi makanan bagi penderita hipertensi. Data yang digunakan meliputi jenis kelamin, umur, berat badan, tinggi badan, jenis aktivitas, tingkat stres, dan tingkat hipertensi pasien. Penelitian ini menggunakan metode reproduksi yang cukup baik untuk diterapkan pada representasi kromosom integer sehingga hasil pencarian yang diberikan terhindar dari solusi optimum lokal. Hasil pengujian menunjukkan bahwa parameter algoritma genetika terbaik adalah populasi sebanyak 15 dengan rata-rata fitness sebesar 20,97, generasi sebanyak 40 dengan rata-rata fitness sebesar 50,10, dan kombinasi crossover rate dan mutation rate sebesar 0,3 dan 0,7 dengan rata-rata fitness sebesar 41,67. Solusi yang dihasilkan adalah berupa komposisi makanan optimal bagi penderita hipertensi

A new selection operator for genetic algorithms that balances between premature convergence and population diversity

The research objective is to find a balance between premature convergence and population diversity with respect to genetic algorithms (GAs). We propose a new selection scheme, namely, split-based selection (SBS) for GAs that ensures a fine balance between two extremes, i.e. exploration and exploitation. The proposed selection operator is further compared with five commonly used existing selection operators. A rigorous simulation-based investigation is conducted to explore the statistical characteristics of the proposed procedure. Furthermore, performance evaluation of the proposed scheme with respect to competing methodologies is carried out by considering 14 diverse benchmarks from the library of the traveling salesman problem (TSPLIB). Based on t-test statistic and performance index (PI), this study demonstrates a superior performance of the proposed scheme while maintaining the desirable statistical characteristics

اختيار العمليات الجينية المناسبة لعمل الخوارزمية الجينية في أمثلة المسائل ذات الارتباط الخطي لجينات الكروموسوم (مسألة تحسين أداء التقاطعات المرورية المنظمة بإشارات مرور ضوئية)

تعتبر الخوارزميات الجينية genetic algorithm إحدى مجالات الذكاء اصطناعي Artificial Intelligence التي تستخدم عملية التطور ونظرية الانتقاء الطبيعي، ويتم استخدامها كأداة فعالة لحل مشاكل التحسين optimization problems. يعد التكامل بين بارامترات (GA) أمرًا حيويًا لنجاح البحث، تتضمن هذه البارامترات معدلات الطفرات mutation والتقاطع crossover وعدد السكان population والتي تعتبر قضايا مهمة في (GA)، كذلك فإن الاختيار الصحيح للعمليات الجينية هو أمر أساسي في فعالية الخوارزمية الجينية. يناقش هذا البحث بناء خوارزمية جينية تخضع لشروط مقيدة في توليد الحلول والعمليات الجينية اللاحقة، يتم من خلالها البحث في تحسين زمن التأخير للعربات التي تعبر التقاطعات المرورية "رباعية الأذرع" المنظمة بإشارات مرور ضمن المحافظات السورية، حيث تم استخدام الخوارزمية في التحكم وأمثلة توزيع الزمن الأخضر لدورة زمنية كاملة للتقاطع المروري على أذرع هذا التقاطع وذك بهدف لتحقيق أقل تأخير زمني ممكن للعربات التي تعبره. تم تمثيل الكروموسوم chromosome بخمس جينات genes أولها زمن دورة الإشارة الضوئية الكلي على التقاطع المروري أما الجينات الأربعة المتبقية فتمثل نسبة الزمن الأخضر من زمن الدورة لكل ذراع من أذرع التقاطع. ترتبط جينات الكروموسوم ببعضها ارتباطاً خطياً، يتم تقييم الكروموسومات عن طريق تابع لياقة fitness function والذي يحدد مدى صلاحيتها بالانتقال إلى الجيل التالي بالمقارنة مع الكروموسومات الأخرى. تم اختيار تابع اللياقة ليكون نموذج رياضي يعبر عن زمن التأخير الكلي على التقاطع [1]، حيث أن الكروموسوم المرتبط بالزمن الأقصر ضمن جيل يشكل الحل الأمثل ضمن هذا الجيل، تم دراسة تأثير حجم الجيل population size وتأثير العمليات الجينية (التزاوج، الطفرة) على عمل الخوارزمية الجينية في حل هذه المسألة للوصول إلى أقل زمن تأخير ممكن

Comparison of crossover operators in genetic algorithm for vehicle routing problems

Genetic algorithm (GA) is a popular metaheuristic with wide-ranging applications, e.g. in routing problems such as traveling salesman problem (TSP) or vehicle routing problem (VRP). Seeking the best combination of parameters in GA application is the key objective in the line of research involving GA. One possible factor to be tested is the operator used for crossover. For VRP, a number of research reporting good performance use the order crossover (OX) operator. For TSP, one paper proposed the modified cycle crossover (CX2) operator and reported that it is better than OX and the partially mapped crossover (PMX). The interest and objective of this paper is to test these three operators in a VRP setting. Excluding the crossover operator, other good principles of GA for VRP obtained from the literature are maintained. The experiment results suggest these findings. Firstly, CX2 is expensive in run time and has difficulty escaping from local optimum but leads to the best fitness value compared to the other operators. Secondly, PMX ranks second both in the fitness performance and run time. Thirdly, while OX has slightly inferior performance, it is able to explore wider search space and therefore still has lots of potential for future research

Integrated computational intelligent paradigm for nonlinear electric circuit models using neural networks, genetic algorithms and sequential quadratic programming

© 2019, Springer-Verlag London Ltd., part of Springer Nature. In this paper, a novel application of biologically inspired computing paradigm is presented for solving initial value problem (IVP) of electric circuits based on nonlinear RL model by exploiting the competency of accurate modeling with feed forward artificial neural network (FF-ANN), global search efficacy of genetic algorithms (GA) and rapid local search with sequential quadratic programming (SQP). The fitness function for IVP of associated nonlinear RL circuit is developed by exploiting the approximation theory in mean squared error sense using an approximate FF-ANN model. Training of the networks is conducted by integrated computational heuristic based on GA-aided with SQP, i.e., GA-SQP. The designed methodology is evaluated to variants of nonlinear RL systems based on both AC and DC excitations for number of scenarios with different voltages, resistances and inductance parameters. The comparative studies of the proposed results with Adam’s numerical solutions in terms of various performance measures verify the accuracy of the scheme. Results of statistics based on Monte-Carlo simulations validate the accuracy, convergence, stability and robustness of the designed scheme for solving problem in nonlinear circuit theory

Plum: Prompt Learning using Metaheuristic

Since the emergence of large language models, prompt learning has become a popular method for optimizing and customizing these models. Special prompts, such as Chain-of-Thought, have even revealed previously unknown reasoning capabilities within these models. However, the progress of discovering effective prompts has been slow, driving a desire for general prompt optimization methods. Unfortunately, few existing prompt learning methods satisfy the criteria of being truly "general", i.e., automatic, discrete, black-box, gradient-free, and interpretable all at once. In this paper, we introduce metaheuristics, a branch of discrete non-convex optimization methods with over 100 options, as a promising approach to prompt learning. Within our paradigm, we test six typical methods: hill climbing, simulated annealing, genetic algorithms with/without crossover, tabu search, and harmony search, demonstrating their effectiveness in black-box prompt learning and Chain-of-Thought prompt tuning. Furthermore, we show that these methods can be used to discover more human-understandable prompts that were previously unknown, opening the door to a cornucopia of possibilities in prompt optimization. We release all the codes in \url{https://github.com/research4pan/Plum}

Solving open travelling salesman subset-tour problem through a hybrid genetic algorithm

In open travelling salesman subset-tour problem (OTSSP), the salesman needs to traverse a set of k (≤n) out of n cities and after visiting the last city, the salesman does not necessarily return to the central depot. The goal is to minimize the overall traversal distance of covering k cities. The OTSSP model comprises two types of problems such as subset selection and permutation of the cities. Firstly, the problem of selection takes place as the salesman’s tours do not contain all the cities. On the other hand, the next problem is about to determine the optimal sequence of the cities from the selected subset of cities. To deal with this problem efficiently, a hybrid nearest neighbor technique based crossover-free Genetic algorithm (GA) with complex mutation strategies is proposed. To the best of the author’s knowledge, this is the first hybrid GA for the OTSSP. As there are no existing studies on OTSSP yet, benchmark instances are not available for OTSSP. For computational experiments, a set of test instances is created by using TSPLIB. The extensive computational results show that the proposed algorithm is having great potential in achieving better results for the OTSSP. Our proposed GA being the first evolutionary-based algorithm that will help as the baseline for future research on OTSSP