Enhancement Political Optimizer Algorithm Based on Leader Redistribution Mechanism

Öz

Optimization algorithms are mathematical steps used to find an optimal solution from a set of possible solutions. These solutions usually require a long time, while optimization algorithms can find solutions quickly. Algorithms are typically combined to overcome algorithm weaknesses or improve performance. In this article, we have enhanced the Political Optimizer (PO) using a mechanism that randomly redistributes the leader's positions to increase the searching efficiency and overcome the problem of stagnation in the local solution. We aimed to redistribute the leaders' locations inside the election campaign process to open the road for the leaders to move to new areas. This idea was applied in critical conditions to have a little effect on the algorithm. The enhancement was promising, and the results improved well for the exploration and exploitation phases. We compared the performance of the enhancement algorithm with the classical political optimizer algorithm beside 13 different state-of-the-art algorithms. The evaluation occurred on 50 benchmark problems 25 were unimodal, and 25 were multi-model objective functions, while the mean result was completed to achieve the stability of the algorithms. The Enhanscemt Political Optimizer (EPO) achieved 23 optimal solutions and 13 close to the optimal solutions, while the results of the PO were 17 optimal and ten solutions close to the optimal. In terms of execution time, the algorithm was very promising, where the execution times in one run are (0.79336 ~ 0.16673) for the unimodal functions, while multi-model functions records (1.0874 ~ 0.16953) of highest and least values, respectively.

Anahtar Kelimeler

• Optimization algorithm
• Political Optimizer
• benchmark problems
• uni-model functions
• multi-model functions

Lider Yeniden Dağıtım Mekanizmasına Dayalı Geliştirme Politik Optimizasyon Algoritması

Öz

Optimizasyon algoritmaları, olası çözümler kümesinden optimum bir çözüm bulmak için kullanılan matematiksel adımlardır. Bu çözümlerin bulunması genellikle çok uzun zaman alırken, optimizasyon algoritmaları çözümleri kısa sürede bulabilir. Algoritmalar genellikle algoritmadaki zayıflıkları gidermek veya performansı iyileştirmek için birleştirilir. Bu makalede, arama verimliliğini artırmak ve yerel çözümdeki durgunluk sorununu aşmak için liderlerin konumlarını rastgele yeniden dağıtan bir mekanizma kullanarak Politik Optimizasyoncuyu (PO) geliştirdik. Liderlerin seçim kampanya süreci içindeki konumlarını yeniden dağıtarak liderlerin yeni alanlara taşınmasına yol açmayı amaçladık. Bu fikir, algoritmaya küçük bir etki yaratmak için kritik koşullarda uygulandı, geliştirme çok umut vericiydi ve sonuçlar hem keşif hem de istismar aşamaları için çok iyi iyileşti. Geliştirme algoritmasının performansını, 13 farklı son teknoloji algoritmanın yanı sıra klasik politik optimizasyon algoritmasıyla karşılaştırdık. Değerlendirme 50 kıyaslama problemi üzerinde yapıldı, 25'i tek modlu ve 25'i çok modelli amaç fonksiyonlarıydı ve ortalama sonuç algoritmaların kararlılığını elde etmek için hesaplıydı. Gelişmiş Politik Optimizasyon (EPO) 23 en iyi çözüme ve en iyiye yakın 13 çözüme ulaşırken, PO'nun sonuçları 17 en iyi ve en iyiye yakın 10 çözümdü. Yürütme süresi açısından algoritma çok ümit vericiydi, tek modlu fonksiyonlar için bir çalıştırmada yürütme süreleri (0.79336 ~ 0.16673) iken, çok modelli fonksiyonlar sırasıyla en yüksek ve en düşük değerleri (1.0874 ~ 0.16953) kaydetti.

Anahtar Kelimeler

• Optimization algorithm
• Political Optimizer
• benchmark problems
• uni-model functions
• multi-model functions

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