Solving forest harvesting problem with artificial intelligence approaches: An example in Taiwan

2021 ◽  
pp. 1-12
Author(s):  
Yi-Chih Hsieh ◽  
Peng-Sheng You ◽  
Hao-Chun Chuang
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Practical Problem ◽  
Forest Harvesting ◽  
Immune Algorithm ◽  
Test Problems ◽  
Hard Problem ◽  
Swarm Optimization ◽  
Np Hard Problem

In this paper, we study the forest harvesting problem (FHP). A forest is assumed to be divided into several identical square units, and each unit has its harvesting value based on its type. Harvesting a unit will affect the growth and values of its neighboring units. In this FHP, the best harvesting plan of a unit must be identified to maximize three various objectives simultaneously. The FHP is a multiobjective mathematical and an NP-hard problem. We apply three artificial intelligence algorithms, namely, immune algorithm, genetic algorithm, and particle swarm optimization, for maximizing the weighted objective to solve the FHP. We also solve the following two sets of test problems: (i) a set of randomly generated FHP problems and (ii) a practical problem in Taiwan. Numerical results show the performance of the three algorithms for the test problems. Finally, we compare and discuss the effects of various weights for the three objectives.

scholarly journals Comparative Analysis of Genetic Algorithm with Swarm Intelligence Algorithms

2021 ◽  
pp. 493-497
Author(s):  
Ganga Devi D
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Comparative Analysis ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Traveling Salesman ◽  
Hard Problem ◽  
Ant Colony Optimization Algorithm ◽  
Swarm Optimization ◽  
Np Hard Problem

Traveling Salesman problem is the most recognized NP-hard problem for the researches in the field of computer science which focused on optimization. TSP finds the minimum travelling distance between given set of cities by traversing each of these cities only once except the starting city. This study discusses the salesman problem by presenting Particle Swarm Optimization Algorithm (PSO), Ant Colony Optimization algorithm (ACO) and Genetic Algorithm (GA) which intends to solve the problem. In this paper, these algorithms are applied on the benchmark TSP dataset Oliver 30. Also, the paper provides comparison between these algorithms and this comparison helps to choose the better algorithm.

Multi-objective integrated planning and scheduling model for operating rooms under uncertainty

2018 ◽  
Vol 232 (9) ◽  
pp. 930-948 ◽  
Author(s):  
Javad Ansarifar ◽  
Reza Tavakkoli-Moghaddam ◽  
Faezeh Akhavizadegan ◽  
Saman Hassanzadeh Amin
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Particle Swarm Optimization ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Operating Rooms ◽  
Programming Approach ◽  
Test Problems ◽  
Swarm Optimization ◽  
Multi Objective

This article formulates the operating rooms considering several constraints of the real world, such as decision-making styles, multiple stages for surgeries, time windows for resources, and specialty and complexity of surgery. Based on planning, surgeries are assigned to the working days. Then, the scheduling part determines the sequence of surgeries per day. Moreover, an integrated fuzzy possibilistic–stochastic mathematical programming approach is applied to consider some sources of uncertainty, simultaneously. Net revenues of operating rooms are maximized through the first objective function. Minimizing a decision-making style inconsistency among human resources and maximizing utilization of operating rooms are considered as the second and third objectives, respectively. Two popular multi-objective meta-heuristic algorithms including Non-dominated Sorting Genetic Algorithm and Multi-Objective Particle Swarm Optimization are utilized for solving the developed model. Moreover, different comparison metrics are applied to compare the two proposed meta-heuristics. Several test problems based on the data obtained from a public hospital located in Iran are used to display the performance of the model. According to the results, Non-dominated Sorting Genetic Algorithm-II outperforms the Multi-Objective Particle Swarm Optimization algorithm in most of the utilized metrics. Moreover, the results indicate that our proposed model is more effective and efficient to schedule and plan surgeries and assign resources than manual scheduling.

Concurrent Societies Based on Genetic Algorithm and Particle Swarm Optimization

2010 ◽  
Vol 14 (1) ◽  
pp. 110-118
Author(s):  
Hrvoje Markovic ◽  
◽  
Fangyan Dong ◽  
Kaoru Hirota
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Nearest Neighbor ◽  
Optimization Problems ◽  
Fitness Function ◽  
Particle Swarm ◽  
Absolute Error ◽  
Population Based ◽  
Test Problems ◽  
Swarm Optimization

A parallel multi-population based metaheuristic optimization framework, called Concurrent Societies, inspired by human intellectual evolution, is proposed. It uses population based metaheuristics to evolve its populations, and fitness function approximations as representations of knowledge. By utilizing iteratively refined approximations it reduces the number of required evaluations and, as a byproduct, it produces models of the fitness function. The proposed framework is implemented as two Concurrent Societies: one based on genetic algorithm and one based on particle swarm optimization both using k -nearest neighbor regression as fitness approximation. The performance is evaluated on 10 standard test problems and compared to other commonly used metaheuristics. Results show that the usage of the framework considerably increases efficiency (by a factor of 7.6 to 977) and effectiveness (absolute error reduced by more than few orders of magnitude). The proposed framework is intended for optimization problems with expensive fitness functions, such as optimization in design and interactive optimization.

Particle Swarm Inspired Timetabling for ICT Courses

2012 ◽  
Vol 263-266 ◽  
pp. 2138-2145
Author(s):  
Oi Mean Foong ◽  
Syamilla Bt Rahim
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Complex Problem ◽  
Hard Problem ◽  
Np Hard ◽  
Swarm Optimization ◽  
University Course Timetabling ◽  
Hard Constraints ◽  
Np Hard Problem ◽  
The Right

University course timetabling is a complex problem which must satisfy a list of constraints in order to allocate the right timeslots and venues for various courses. The challenge is to make the NP-hard problem user-friendly, highly interactive and faster run time complexity of algorithm. The objective of the paper is to propose Particle Swarm Optimization (PSO) timetabling model for Undergraduate Information and Communication Technology (ICT) courses. The PSO model satisfies hard constraints with minimal violation of soft constraints. Empirical results show that the rds: NP hard problem, timetabling, particle swarm optimization

scholarly journals Improving the Gridshells’ Regularity by Using Evolutionary Techniques

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 440
Author(s):  
Marjan Goodarzi ◽  
Ali Mohades ◽  
Majid Forghani-elahabad
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Gaussian Curvature ◽  
Particle Swarm ◽  
Experimental Results ◽  
Length Ratio ◽  
Test Problems ◽  
Swarm Optimization ◽  
Shape Ratio ◽  
Almost All

Designing and optimizing gridshell structures have been very attractive problems in the last decades. In this work, two indexes are introduced as “length ratio” and “shape ratio” to measure the regularity of a gridshell and are compared to the existing indexes in the literature. Two evolutionary techniques, genetic algorithm (GA) and particle swarm optimization (PSO) method, are utilized to improve the gridshells’ regularity by using the indexes. An approach is presented to generate the initial gridshells for a given surface in MATLAB. The two methods are implemented in MATLAB and compared on three benchmarks with different Gaussian curvatures. For each grid, both triangular and quadrangular meshes are generated. Experimental results show that the regularity of some gridshell is improved more than 50%, the regularity of quadrangular gridshells can be improved more than the regularity of triangular gridshells on the same surfaces, and there may be some relationship between Gaussian curvature of a surface and the improvement percentage of generated gridshells on it. Moreover, it is seen that PSO technique outperforms GA technique slightly in almost all the considered test problems. Finally, the Dolan–Moré performance profile is produced to compare the two methods according to running times.

scholarly journals Evolutionary artificial intelligence algorithms for the one-way road orientation planning problem with multiple venues: An example of evacuation planning in Taiwan

2021 ◽  
Vol 104 (3_suppl) ◽  
pp. 003685042110632
Author(s):  
Yi-Chih Hsieh ◽  
Peng-Sheng You
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
The Other ◽  
Test Problems ◽  
Planning Problem ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Swarm Optimization ◽  
Encoding Scheme ◽  
The One

Introduction: In large-scale events such as concerts and sports competitions, participants often leave the venue at the same time to return to their respective destinations. Improper traffic planning and traffic light operation usually lead to traffic congestion and road chaos near the sites. Rapid evacuation of participants has become an important issue. Objectives: In this work, a one-way road orientation planning problem with multiple venues is studied in which all roads near the venues are to be scheduled into a one-way orientation with strong connectivity to increase the evacuation efficiency of participants. Methods: In accordance with Robbins’ theorem and a random sequence of integers, an encoding scheme based on module operator is presented to construct a strongly connected graph and plan a one-way orientation for all roads. The proposed encoding scheme is further embedded into four artificial intelligence approaches, namely, grey wolf optimization, immune algorithm, genetic algorithm, and particle swarm optimization, to solve the one-way road orientation planning problem such that the total distance of all vehicles from venues to their destinations is minimized. Results: Numerical results of test problems with multiple venues in Taiwan are provided and analyzed. As shown, all four algorithms can obtain the best solution for the test problems. Conclusions: The new presented encoding scheme with four algorithms can be used to effectively solve the one-way road orientation planning problem for the evacuation of participants. Moreover, grey wolf optimization is superior to the other three algorithms and particle swarm optimization is faster than the other three algorithms.

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