Identification of partial shading in solar panels using genetic algorithms, simulated annealing, and particle swarm optimisation

Harmony search (HS) was introduced in 2001 as a heuristic population-based optimisation algorithm. Since then HS has become a popular alternative to other heuristic algorithms like simulated annealing and particle swarm optimisation. However, some flaws, like the need for parameter tuning, were identified and have been a topic of study for much research over the last 10 years. Many variants of HS were developed to address some of these flaws, and most of them have made substantial improvements. In this paper we compare the performance of three recent HS variants: exploratory harmony search, self-adaptive harmony search, and dynamic local-best harmony search. We compare the accuracy of these algorithms, using a set of well-known optimisation benchmark functions that include both unimodal and multimodal problems. Observations from this comparison led us to design a novel hybrid that combines the best attributes of these modern variants into a single optimiser called generalised adaptive harmony search.

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Particle swarm optimisation algorithm and multi-start simulated annealing algorithm for scheduling batches of parts in multi-cell flexible manufacturing system

International Journal of Services and Operations Management ◽

10.1504/ijsom.2019.10018082 ◽

2019 ◽

Vol 32 (1) ◽

pp. 83

Author(s):

A.N. Balaji ◽

S. Porselvi ◽

N. Jawahar

Keyword(s):

Simulated Annealing ◽

Flexible Manufacturing ◽

Manufacturing System ◽

Flexible Manufacturing System ◽

Simulated Annealing Algorithm ◽

Particle Swarm ◽

Particle Swarm Optimisation ◽

Optimisation Algorithm ◽

Annealing Algorithm ◽

Particle Swarm Optimisation Algorithm

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Optimising monthly tilt angles of solar panels using particle swarm optimisation algorithm

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v23.i1.pp75-89 ◽

2021 ◽

Vol 23 (1) ◽

pp. 75

Author(s):

Namruta S. Kanianthara ◽

Swee Peng Ang ◽

Ashraf Fathi Khalil Sulayman ◽

Zainidi bin Hj. Abd. Hamid

Keyword(s):

Tilt Angle ◽

Global Minimum ◽

Particle Swarm ◽

Pso Algorithm ◽

Particle Swarm Optimisation ◽

Solar Panels ◽

Abc Algorithm ◽

Optimisation Algorithm ◽

Brunei Darussalam ◽

Particle Swarm Optimisation Algorithm

This paper presents an intelligent computational method using the PSO (particle swarm optimisation) algorithm to determine the optimum tilt angle of solar panels in PV systems. The objective of the paper is to assess the performance of this method against conventional methods of determining the optimum tilt angle. The method presented here can be used to determine the optimum tilt angle at any location around the world. In this paper, it was applied to Brunei Darussalam, and succeeded in computing monthly optimum tilt angles, ranging from 34.7ᵒ in December to -26.7ᵒ in September. Results showed that changing the tilt angle every month, as determined by the PSO algorithm, increased annual yield by: (i) 5.94%, compared to keeping it fixed at 0ᵒ, (ii) 8.65%, compared to Lunde’s method and (iii) 17.31%, compared to Duffie and Beckman’s method. Benchmark test functions were used to compare and evaluate the performance of the PSO algorithm with the artificial bee colony (ABC) algorithm, another metaheuristic algorithm. The tests revealed that the PSO algorithm outperformed the ABC algorithm, exhibiting lower root mean square error and standard deviation, better convergence to the global minimum, more accurate location of the global minimum, and faster execution times.

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Finding Solutions to Sudoku Puzzles Using Human Intuitive Heuristics

South African Computer Journal ◽

10.18489/sacj.v49i0.111 ◽

2012 ◽

Vol 49 ◽

Author(s):

Nelishia Pillay

Keyword(s):

Genetic Algorithms ◽

Particle Swarm Optimization ◽

Simulated Annealing ◽

Genetic Programming ◽

Harmony Search ◽

Particle Swarm ◽

Problem Instance ◽

Initial Population ◽

Swarm Optimization

Sudoku is a logical puzzle that has achieved international popularity. Given this, there have been a number of computer solvers developed for this puzzle. Various methods including genetic algorithms, simulated annealing, particle swarm optimization and harmony search have been evaluated for this purpose. The approach described in this paper combines human intuition and optimization to solve Sudoku problems. The main contribution of this paper is a set of heuristic moves, incorporating human expertise, to solve Sudoku puzzles. The paper investigates the use of genetic programming to optimize a space of programs composed of these heuristics moves, with the aim of evolving a program that can produce a solution to the Sudoku problem instance. Each program is a combination of randomly selected moves. The approach was tested on 1800 Sudoku puzzles of differing difficulty. The approach presented was able to solve all 1800 problems, with a majority of these problems being solved in under a second. For a majority of the puzzles evolution was not needed and random combinations of the moves created during the initial population produced solutions. For the more difficult problems at least one generation of evolution was needed to find a solution. Further analysis revealed that solution programs for the more difficult problems could be found by enumerating random combinations of the move operators, however at a cost of higher runtimes. The performance of the approach presented was found to be comparable to other methods used to solve Sudoku problems and in a number of cases produced better results.

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