BSSA: Binary spring search algorithm

Dehghani, Mohammad; Montazeri, Zeinab; Dehghani, Ali; Nouri, Nima; Seifi, Ali Reza

doi:10.1109/kbei.2017.8324977

Cited by 25 publications

(25 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spring Search Algorithm (SSA) [6] Inspired by Hooke's law. In SSA search agents are group of weights which connected together with springs [16]. Some of the other this category algorithms are: Galaxy-based Search Algorithm (GbSA) [17], Curved Space Optimization (CSO) [18], Artificial Chemical Reaction Optimization Algorithm (ACROA) [19], Small World Optimization Algorithm (SWOA) [20] and Central Force Optimization (CFO) [21].…”

Section: Physics-based Algorithmsmentioning

confidence: 99%

DGO: Dice Game Optimizer

Dehghani¹,

Montazeri²,

Malik³

2019

Gazi University Journal of Science

Self Cite

View full text Add to dashboard Cite

Highlights• The paper focuses on proposing a new optimization algorithm called Dice Game Optimizer (DGO). • DGO can be used to solve optimization constrained and unconstrained problems in different sciences. • The proposed DGO algorithm is tested on 23 standard benchmark test functions. • The performance of DGO is also examined on one engineering design problem (Pressure vessel design).• The results show the merits of the DGO algorithm compared to the existing algorithms. Article Info AbstractIn recent years, optimization algorithms have been used in many applications. Most of these algorithms are inspired by physical processes or living beings' behaviors. This article suggests a new optimization method called "Dice Gaming Optimizer" (DGO), which simulates dice gaming laws. This algorithm is inspired by an old game and the searchers are a set of players. Each player moves in the playground based on at least one and maximum six different players called guide's players. The number of guide's players for each player is determined by the number of dice. DGO is tested on 23 standard benchmark test functions and also compared with eight other algorithms such as: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Cuckoo Search (CS), Ant-Lion Optimizer (ALO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm and Emperor Penguin Optimizer (EPO). Moreover, a reallife engineering design problem is solved by DGO. The results indicate that DGO have better performance as compared to the other well-known optimization algorithms.

show abstract

Section: Physics-based Algorithmsmentioning

confidence: 99%

DGO: Dice Game Optimizer

Dehghani¹,

Montazeri²,

Malik³

2019

Gazi University Journal of Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…These phenomena encompass various natural processes such as animal behavior (e.g., Donkey Theorem Optimization (DTO) [4], Honey Bees Mating (HBM) [5], Shuffled Frog Leaping Algorithm (SFLA) [6], Hunting Search (HS) [7], Ant Colony Optimization (ACO) [8], and Particle Swarm Optimization (PSO) [9]), biological processes (e.g., Genetic Algorithm (GA) [10], Differential Evolution (DE) [11], Evolution Strategy (ES) [12], Genetic Programming (GP) [13], and Biogeography-based Optimizer (BBO) [14], Artificial Immune Systems (AIS) [15]), the physical process (e.g. Spring Search Algorithm (SSA) [16,17], Simulated Annealing (SA) [18]), the musical process (i.e. Harmony Search (HS) [19][20][21][22]), population process (i.e.…”

Section: Introductionmentioning

confidence: 99%

Momentum search algorithm: a new meta-heuristic optimization algorithm inspired by momentum conservation law

Dehghani

Samet

2020

SN Appl. Sci.

Self Cite

View full text Add to dashboard Cite

A novel optimization methodology, Momentum Search Algorithm (MSA) is presented based on Newton's laws: the law of conservation of momentum. It includes a set of masses in a closed system considering the conservation of momentum and kinetic energy of bodies. The possible solutions are presented by system bodies' positions in an n-dimensional space. The mass of bodies is proportional to their fitness function. Larger masses represent the better solutions. At each iteration, an external body collides separately with all solution bodies and moves them toward the optimum solution. The direction of the collision depends on the position of solution bodies and the position of the body with the best fitness function. As the better solutions have heavier bodies, the external body has less effect on their positions. On the other hand, the worse solutions are lighter and moved easily by the external body toward the better positions. The best position is achieved by allowing the external body to move the solution bodies toward better positions. The numerical results obtained from several standard benchmark test functions indicate the superiority of the proposed method over many other optimization techniques such as Genetic Algorithm,

show abstract

“…Researchers have also used other methods to solve the UC problem such as: Particle Swarm Optimization (PSO) [15], Teaching Learning Based Optimization (TLBO) [16], Gravitational Search Algorithm (GSA) [17] , Water Cycle Algorithm (WCA) [18] and Grey Wolf Optimization (GWO) [19], Whale Optimization Algorithm (WOA) [20]. Other algorithms are also suggested for UC solving [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Energy Commitment: A Planning of Energy Carrier Based on Energy Consumption

Dehghani

Montazeri

Malik

2019

Elektroteh. elektromeh.

View full text Add to dashboard Cite

Purpose. Energy consumption is one of the criteria for determining the quality of life in a country. Continued supply of energy and the possibility of long-term access to resources require a comprehensive plan. One of the key issues in the field of energy planning is energy carriers. In this paper, a new theory is introduced to energy network studies for planning of energy carriers called Energy Commitment. In this theory, an appropriate planning is applied for energy carriers based the final energy consumption. Energy carriers are available either naturally or after the energy conversion process. Energy commitment is modeled on an energy network with the presence of electrical energy,

show abstract

BSSA: Binary spring search algorithm

Cited by 25 publications

References 11 publications

DGO: Dice Game Optimizer

DGO: Dice Game Optimizer

Momentum search algorithm: a new meta-heuristic optimization algorithm inspired by momentum conservation law

Energy Commitment: A Planning of Energy Carrier Based on Energy Consumption

Contact Info

Product

Resources

About