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A perturbed particle swarm optimization using harmony search and mutation
(2020-07-01)
Phuchan, Wikrom
;
Kruatrachue, Boontee
This paper applies the harmony search (HS) and mutation to lessens the stagnant of PSO. When a particle stops improving, it is mutated or replace by a position value created from harmony memory, which memorized improve locations of particles (Pbest) in search space. Another HS generates the best position among all particles (Gbest) to replace stagnant Gbest to sway the swarm from the trapping location. This HS has another harmony memory that contains improving Gbest. The results of the proposed algorithm are compared with related modified-PSO using 27 benchmark functions. The proposed algorithm locates optimum in more benchmark functions with faster execution.
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Mutation Variations in Improving Local Optima Problem of PSO
(2020-01-01)
Adsawinnawanawa, Ekkarat
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Kruatrachue, Boontee
This paper experiment on various concepts in performing mutation to lessen trap in a local optima problem of Particle swarm optimization (PSO). The first concept is when to perform mutation. The earlier mutation favors exploration more than exploitation and usually leads to slow convergence, while the late mutation tends to have opposite characteristics. The second concept is the reset of a known best position (GBEST) when trapping in local optima. The reset reduces the chance of trapping in the same local optima but may lead to slower convergence. On the other hand, mutations without reset best position exploit previous knowledge and converge faster if the GBEST closes to optima. The performances of each concept are compared using 27 benchmark test functions. The results are mixing, but the early mutation without reset GBEST perform better in many of test function.
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Improved Rerun Particle Swarm Optimization Algorithm with Harmony Search
(2019-04-10)
Phuchan, Wikrom
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Kruatrachue, Boontee
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Siriboon, Kritawan
One of the most crucial problem of the particle swarm optimization is that it can easily trap in local optima. There are some studies tried to resolve the drawback using the reset and mutation mechanism. Although it unraveled the problem well when the optimal position of benchmark function is at zeros in all dimensions. It cannot locate optima when the exact answer randomly shifted over the possible region. This study integrates Harmony Search (HS) to the rerun and reset mechanism to PSO. It performs well with selected benchmark functions, when the optimal position is randomly shifted in most of the test functions. Since it does not favor optimal point at zeroes when the optimal position is not shifted, the number of calls of the evaluation function is more than the other algorithms in some cases.
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Enhance particle's exploration of particle swarm optimization with individual particle mutation
(2019-03-01)
Adsawinnawanawa, Ekkarat
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Kruatrachue, Boontee
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Siriboon, Kritawan
This paper proposed the restart of slow improvement particle with Mutation of its position. This can alleviate the trapped in local optima problem of Particle swarm optimization (PSO). The main characteristic of this method is the gradually restart of particle instead of restart all particle at the same time (rerun). This seems to maintain fast convergence of PSO and avoid overhead of restart the whole swarm. The performance of the proposed method is compared to others algorithms with 26 benchmark test functions. The results confirm the faster convergence with optimum results of the proposed method in most test functions.
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Improving Multi-Swarm by Slightly Mutation Particle and GBEST of Stuck Swarm Along with Randomly Selecting GBEST of Other Swarm
(2018-08-21)
Chengkhuntod, Kanokporn
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Kruatrachue, Boontee
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Siriboon, Kritawan
This paper proposed another approach in handling trapping in local optimum problem of Particle Swarm Optimization (PSO) using multi-swarm. Since each swarm might trap in different local optimum, the trapped swarm restart with slightly mutation (15% of each particle attributes) along with swaying swarm by randomly use of other swarm GBEST position. In the case of all swarm trapping in the same location, the trap GBEST is also slightly mutate in the same way as particle position. This proposed technique is tested on a set of twenty-four benchmark test functions. The experimental results show that the proposed method is better than other comparing methods.
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Combine multi particle swarm in supporting trapping in local optima
(2018-08-13)
Poempool, Lukkana
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Kruatrachue, Boontee
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Siriboon, Kritawan
This paper proposed using multi swarm to lessen trapping in local optima problem of Particle Swarm Optimization (PSO). The use of multiple swarms can increase wider global search at the cost of decrease narrower local search. Hence, the use of multiple swarms alone can't solve the trapping problem. This paper extends the use of multi swarm by merging all the swarm into single group to enhanced local search when needed. This can increase the local search ability of multi swarm and increase the chance of the trapped swarm to move to other nearby local optima. The proposed method is compared to similarly comparable modified PSO and other similarly multi particle swarm without merging using 26 benchmark functions.
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The use of global best position in rerun of particle swarm optimization
(2018-08-13)
Cheypoca, Varothon
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Siriboon, Kritawan
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Kruatrachue, Boontee
This paper studies the use of particle best position (GBEST) in rerun when particle swarm optimization (PSO) traps in local optima. Reinitialize particles positions are often used to restart PSO to get better results when trapping in local optima. This paper proposed the use of GBEST to further force particle movement out of previous local optima instead of only reset GBEST. The proposed method is tested on 26 benchmark test functions with satisfactory results.
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Hybrid multi-swarm with Harmony Search algorithm
(2017-11-03)
Phuchan, Wikrom
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Kruatrachue, Boontee
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Siriboon, Kritawan
This paper proposed a new metaheuristic algorithm, Hybrid Multi-swarm with Harmony Search algorithm which combines two famous metaheuristics, particle swarm optimization (PSO) and Harmony Search algorithm (HS). The main advantage of PSO is its convergence speed while its main drawback is trapping in local optimum problem. To improve PSO performance, this research use HS to increase PSO diversity and extend its convergence point to a better local optimum. The proposed algorithm was compared with three related algorithms on the optimization benchmark functions. The experiment results show the proposed algorithm yields better fitness value solution.
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A modified multi-swarm optimization with interchange GBEST and particle redistribution
(2017-10-19)
Chengkhuntod, Kanokporn
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Kruatrachue, Boontee
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Siriboon, Kritawan
The Particle Swarm Optimization (PSO) is an optimization algorithm using multiples particle to search solution space for an optimize solution. Each particle of PSO moves toward the best solution within its group. For this behavior, PSO often traps in local optimum. Many researchers proposed splitting a swarm into multiple swarms so that they may move to different local optimum. Besides, the mutation operation technique, the natural selection technique and the crossover operation technique are added to normal PSO process. These proposed techniques are called Selective Crossover base on Fitness in Multi-Swarm Optimization (SFMPSO) and Fast Multi-swarm Optimization (FMPSO). However, both techniques used too many evaluation calls dues to crossover and the mutation operation. This paper proposes setting the best position (GBEST) of a trapped swarm to GBEST of the other swarm. Then, the swarm's particle is redistributed in solution space before restart the trapped swarm. This proposed technique is evaluated on a set of twenty-six benchmark test functions. The experimental results show that the results are better than those of PSO, FMPSO and SFMPSO.
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A modified particle swarm optimization with mutation and reposition
(2014-12-01)
Ratanavilisagul, Chiabwoot
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Kruatrachue, Boontee
The common problems of particle swarm optimization (PSO) are those of trapping in local optimum and premature convergence. This research paper aims to develop a solution to both problems by introducing mutation around particles and employing the reposition technique. The concurrent use of the introduced mutation and reposition has proved to solve both problems and enhanced the PSO performance; and thus is employed in this research. The proposed technique is termed MRPSO. MRPSO is tested on sixteen benchmark functions and the multidimensional knapsack problems (MKP). MRPSO yields the more satisfactory search results than the genetic algorithm (GA) and PSOs for the benchmark functions and the MKPs.

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