Genetic algorithm for networks with dynamic mutation rate

Abstract

A genetic algorithm based on hydraulic optimization is applied in the paper in order to achieve the lowest possible costs, the most appropriate pipe diameter, and the most favourable longitudinal slope values. A new algorithm for mutation operation, called the dynamic mutation rate method, is proposed as a means to reduce the number of trials for genetic algorithm parameters, especially for mutation rates, and to obtain an optimum value in the shortest possible time. Genetski algoritam za mreže s dinamičkom mutacijomProjektiranje oborinskih i sanitarnih sustava odvodnje temelji se prvenstveno na odluci o mrežnom sustavu i na hidrauličkim proračunima za cijevi koje će se koristiti u mreži. U radu se analizira genetski algoritam baziran na hidrauličkoj optimizaciji kako bi se postigli najniži troškovi te najprikladniji promjeri cijevi i najpovoljnije vrijednosti uzdužnog nagiba. Predlaže se novi algoritam za provedbu mutacije, nazvan "metoda dinamičke mutacije", kako bi se smanjio broj ispitivanja parametara genetskog algoritma, što se naročito odnosi na brzinu mutacije, te postigla optimalna vrijednost u što kraćem razdoblju. Genetischer Algorithmus für Netzwerke mit dynamischer MutationIn der Arbeit kommt ein genetischer Algorithmus zur Anwendung, der auf einer hydraulischen Optimierung beruht, um das günstigste Kostenverhältnis zu erreichen, den angemessensten Rohrdurchmesser und die günstigsten Werte der Längsneigung zu ermitteln. Es wird ein neuer Algorithmus zur Umsetzung der Mutation bzw. die "Methode der dynamischen Mutation" vorgeschlagen, um die Zahl der Untersuchungen der Parameter des genetischen Algorithmus zu reduzieren, was sich in erster Linie auf die Geschwindigkeit der Mutation bezieht, sowie zur Ermittlung von optimalen Werten.

Analyzing the Effect of Sewer Network Size on Optimization Algorithms’ Performance in Sewer System Optimization

“…The first approach in research is obtaining all the variables simultaneously [6][7][8][9][10]. The second approach is performing the hydraulic optimization to determine the pipe diameters and slopes or cover depths with a predefined layout [11][12][13][14][15][16][17][18][19][20]. The last approach is layout optimization only [21][22][23][24][25].…”

“…In the related literature, many metaheuristic algorithms, namely, the genetic algorithm [5,12,13,18,28], cellular automata [20,[29][30][31], ant colony algorithm [15,[32][33][34], and particle swarm optimization algorithm [7,35], are utilized to solve sewer optimization problems.…”

Optimization of Dual-Design Operation Ventilation System Network Based on Improved Genetic Algorithm

“…Previous studies have used genetic algorithms to optimize duct diameter combinations [19][20][21]. There are various improved genetic algorithms such as using adaptive penalty functions [22], improved crossover operator [23] and mutation operators [24,25], as well as in combination with hydraulic simulation software such as EPANET 2.2 [26]. However, the optimization objects of previous studies are typically urban water supply networks, while this study focuses on DOV networks.…”

Optimum Cost Prediction of Reinforced Concrete Cantilever Retaining Walls

“…In the design of RCCRWs, the selection of the appropriate wall dimensions is crucial since it dramatically affects both the stability and cost. Optimization techniques have found extensive applications in different areas of civil engineering, such as project time-cost estimation [1], risk-cost maintenance strategy [2], optimization of structures [3][4][5][6], damage detection [7] and water distribution network optimization [8]. Numerous studies have been conducted to optimize the size of retaining walls, with the aim of achieving cost-effective designs [9].…”