Snow Leopard Optimization Algorithm: A New Nature-Based Optimization Algorithm for Solving Optimization Problems

Numerous optimization problems have been defined in different disciplines of science that must be optimized using effective techniques. Optimization algorithms are an effective and widely used method of solving optimization problems that are able to provide suitable solutions for optimization problems. In this paper, a new nature-based optimization algorithm called Snow Leopard Optimization Algorithm (SLOA) is designed that mimics the natural behaviors of snow leopards. SLOA is simulated in four phases including travel routes, hunting, reproduction, and mortality. The different phases of the proposed algorithm are described and then the mathematical modeling of the SLOA is presented in order to implement it on different optimization problems. A standard set of objective functions, including twenty-three functions, is used to evaluate the ability of the proposed algorithm to optimize and provide appropriate solutions for optimization problems. Also, the optimization results obtained from the proposed SLOA are compared with eight other well-known optimization algorithms. The optimization results show that the proposed SLOA has a high ability to solve various optimization problems. Also, the analysis and comparison of the optimization results obtained from the SLOA with the other eight algorithms shows that the SLOA is able to provide more appropriate quasi-optimal solutions and closer to the global optimal, and with better performance, it is much more competitive than similar algorithms.

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Teamwork Optimization Algorithm: A New Optimization Approach for Function Minimization/Maximization

Sensors ◽

10.3390/s21134567 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4567

Author(s):

Mohammad Dehghani ◽

Pavel Trojovský

Keyword(s):

Optimization Algorithm ◽

Optimization Problems ◽

Optimization Algorithms ◽

Main Idea ◽

Optimal Solution ◽

Population Based ◽

Function Minimization ◽

Optimization Approach ◽

Objective Functions ◽

Simulation Results

Population-based optimization algorithms are one of the most widely used and popular methods in solving optimization problems. In this paper, a new population-based optimization algorithm called the Teamwork Optimization Algorithm (TOA) is presented to solve various optimization problems. The main idea in designing the TOA is to simulate the teamwork behaviors of the members of a team in order to achieve their desired goal. The TOA is mathematically modeled for usability in solving optimization problems. The capability of the TOA in solving optimization problems is evaluated on a set of twenty-three standard objective functions. Additionally, the performance of the proposed TOA is compared with eight well-known optimization algorithms in providing a suitable quasi-optimal solution. The results of optimization of objective functions indicate the ability of the TOA to solve various optimization problems. Analysis and comparison of the simulation results of the optimization algorithms show that the proposed TOA is superior and far more competitive than the eight compared algorithms.

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A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

Applied Sciences ◽

10.3390/app11104382 ◽

2021 ◽

Vol 11 (10) ◽

pp. 4382

Author(s):

Ali Sadeghi ◽

Sajjad Amiri Doumari ◽

Mohammad Dehghani ◽

Zeinab Montazeri ◽

Pavel Trojovský ◽

...

Keyword(s):

Optimization Algorithm ◽

Optimization Problems ◽

Search Algorithm ◽

Fitness Function ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Gray Wolf ◽

Good Group ◽

Good Ability ◽

Whale Optimization

Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.

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GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Mathematics ◽

10.3390/math9111190 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1190

Author(s):

Mohammad Dehghani ◽

Zeinab Montazeri ◽

Štěpán Hubálovský

Keyword(s):

Optimization Algorithm ◽

Optimization Problems ◽

Search Algorithm ◽

Random Search ◽

Optimization Algorithms ◽

Gravitational Search Algorithm ◽

Main Idea ◽

Population Based ◽

Grey Wolf Optimizer ◽

Whale Optimization

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms.

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GBUO: “The Good, the Bad, and the Ugly” Optimizer

Applied Sciences ◽

10.3390/app11052042 ◽

2021 ◽

Vol 11 (5) ◽

pp. 2042

Author(s):

Hadi Givi ◽

Mohammad Dehghani ◽

Zeinab Montazeri ◽

Ruben Morales-Menendez ◽

Ricardo A. Ramirez-Mendoza ◽

...

Keyword(s):

High Dimension ◽

Essential Role ◽

Optimization Problems ◽

Optimization Algorithms ◽

Stochastic Search ◽

Objective Functions ◽

Science And Engineering ◽

Fixed Dimension ◽

Dimension Functions ◽

Simulation Results

Optimization problems in various fields of science and engineering should be solved using appropriate methods. Stochastic search-based optimization algorithms are a widely used approach for solving optimization problems. In this paper, a new optimization algorithm called “the good, the bad, and the ugly” optimizer (GBUO) is introduced, based on the effect of three members of the population on the population updates. In the proposed GBUO, the algorithm population moves towards the good member and avoids the bad member. In the proposed algorithm, a new member called ugly member is also introduced, which plays an essential role in updating the population. In a challenging move, the ugly member leads the population to situations contrary to society’s movement. GBUO is mathematically modeled, and its equations are presented. GBUO is implemented on a set of twenty-three standard objective functions to evaluate the proposed optimizer’s performance for solving optimization problems. The mentioned standard objective functions can be classified into three groups: unimodal, multimodal with high-dimension, and multimodal with fixed dimension functions. There was a further analysis carried-out for eight well-known optimization algorithms. The simulation results show that the proposed algorithm has a good performance in solving different optimization problems models and is superior to the mentioned optimization algorithms.

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Adaptive Grouping Brain Storm Optimization for Multimodal Optimization Problems

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2021100105 ◽

2021 ◽

Vol 12 (4) ◽

pp. 81-100

Author(s):

Yao Peng ◽

Zepeng Shen ◽

Shiqi Wang

Keyword(s):

Optimization Problem ◽

Optimization Problems ◽

Optimal Algorithm ◽

Peak Detection ◽

Multimodal Optimization ◽

Optimal Solutions ◽

Grouping Strategy ◽

Different Dimensions ◽

Global Optimal ◽

Localization Ability

Multimodal optimization problem exists in multiple global and many local optimal solutions. The difficulty of solving these problems is finding as many local optimal peaks as possible on the premise of ensuring global optimal precision. This article presents adaptive grouping brainstorm optimization (AGBSO) for solving these problems. In this article, adaptive grouping strategy is proposed for achieving adaptive grouping without providing any prior knowledge by users. For enhancing the diversity and accuracy of the optimal algorithm, elite reservation strategy is proposed to put central particles into an elite pool, and peak detection strategy is proposed to delete particles far from optimal peaks in the elite pool. Finally, this article uses testing functions with different dimensions to compare the convergence, accuracy, and diversity of AGBSO with BSO. Experiments verify that AGBSO has great localization ability for local optimal solutions while ensuring the accuracy of the global optimal solutions.

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Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.984-985.419 ◽

2014 ◽

Vol 984-985 ◽

pp. 419-424

Author(s):

P. Sabarinath ◽

M.R. Thansekhar ◽

R. Saravanan

Keyword(s):

Design Optimization ◽

Optimization Problems ◽

Optimization Methods ◽

Optimal Solutions ◽

Objective Functions ◽

Nsga Ii ◽

Multi Objective ◽

Total Displacement ◽

Design Variables ◽

Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

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An evolutionary-based optimization algorithm for truss sizing design

Vietnam Journal of Mechanics ◽

10.15625/0866-7136/7476 ◽

2016 ◽

Vol 38 (4) ◽

pp. 307-317

Author(s):

Pham Hoang Anh

Keyword(s):

Local Search ◽

Objective Function ◽

Optimization Algorithm ◽

Optimization Problems ◽

Optimal Solution ◽

The Other ◽

Truss Structures ◽

Benchmark Problems ◽

Discrete Variables ◽

Objective Functions

In this paper, the optimal sizing of truss structures is solved using a novel evolutionary-based optimization algorithm. The efficiency of the proposed method lies in the combination of global search and local search, in which the global move is applied for a set of random solutions whereas the local move is performed on the other solutions in the search population. Three truss sizing benchmark problems with discrete variables are used to examine the performance of the proposed algorithm. Objective functions of the optimization problems are minimum weights of the whole truss structures and constraints are stress in members and displacement at nodes. Here, the constraints and objective function are treated separately so that both function and constraint evaluations can be saved. The results show that the new algorithm can find optimal solution effectively and it is competitive with some recent metaheuristic algorithms in terms of number of structural analyses required.

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An enhanced surrogate-assisted differential evolution for constrained optimization problems

10.21203/rs.3.rs-522951/v1 ◽

2021 ◽

Author(s):

Rafael de Paula Garcia ◽

Beatriz Souza Leite Pires de Lima ◽

Afonso Celso de Castro Lemonge ◽

Breno Pinheiro Jacob

Keyword(s):

Differential Evolution ◽

Optimization Problems ◽

Optimization Procedure ◽

Nearest Neighbors ◽

Engineering Optimization ◽

Optimal Solutions ◽

Objective Functions ◽

Constrained Optimization Problems ◽

Engineering Problems ◽

Complex Engineering

Abstract The application of Evolutionary Algorithms (EAs) to complex engineering optimization problems may present difficulties as they require many evaluations of the objective functions by computationally expensive simulation procedures. To deal with this issue, surrogate models have been employed to replace those expensive simulations. In this work, a surrogate-assisted evolutionary optimization procedure is proposed. The procedure combines the Differential Evolution method with a Anchor -nearest neighbors ( –NN) similarity-based surrogate model. In this approach, the database that stores the solutions evaluated by the exact model, which are used to approximate new solutions, is managed according to a merit scheme. Constraints are handled by a rank-based technique that builds multiple separate queues based on the values of the objective function and the violation of each constraint. Also, to avoid premature convergence of the method, a strategy that triggers a random reinitialization of the population is considered. The performance of the proposed method is assessed by numerical experiments using 24 constrained benchmark functions and 5 mechanical engineering problems. The results show that the method achieves optimal solutions with a remarkably reduction in the number of function evaluations compared to the literature.

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Genetic Algorithm and Other Meta-Heuristics

Successful Strategies in Supply Chain Management ◽

10.4018/978-1-59140-303-6.ch007 ◽

2005 ◽

pp. 144-173 ◽

Cited By ~ 1

Author(s):

Bernard K.S. Cheung

Keyword(s):

Genetic Algorithm ◽

Large Scale ◽

Optimization Problems ◽

Optimal Solution ◽

Schema Theory ◽

Heuristic Methods ◽

Optimal Solutions ◽

Corporate Globalization ◽

Global Optimal ◽

Mutation And Selection

Genetic algorithms have been applied in solving various types of large-scale, NP-hard optimization problems. Many researchers have been investigating its global convergence properties using Schema Theory, Markov Chain, etc. A more realistic approach, however, is to estimate the probability of success in finding the global optimal solution within a prescribed number of generations under some function landscapes. Further investigation reveals that its inherent weaknesses that affect its performance can be remedied, while its efficiency can be significantly enhanced through the design of an adaptive scheme that integrates the crossover, mutation and selection operations. The advance of Information Technology and the extensive corporate globalization create great challenges for the solution of modern supply chain models that become more and more complex and size formidable. Meta-heuristic methods have to be employed to obtain near optimal solutions. Recently, a genetic algorithm has been reported to solve these problems satisfactorily and there are reasons for this.

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Disruption-Based Multiobjective Equilibrium Optimization Algorithm

Computational Intelligence and Neuroscience ◽

10.1155/2020/8846250 ◽

2020 ◽

Vol 2020 ◽

pp. 1-21

Author(s):

Hao Chen ◽

Weikun Li ◽

Weicheng Cui

Keyword(s):

Multiobjective Optimization ◽

Optimization Algorithm ◽

Optimization Problems ◽

Novel Mutation ◽

Optimization Algorithms ◽

Benchmark Problems ◽

Engineering Optimization ◽

Test Results ◽

Nature Inspired Computing ◽

Disruption Method

Nature-inspired computing has attracted huge attention since its origin, especially in the field of multiobjective optimization. This paper proposes a disruption-based multiobjective equilibrium optimization algorithm (DMOEOA). A novel mutation operator named layered disruption method is integrated into the proposed algorithm with the aim of enhancing the exploration and exploitation abilities of DMOEOA. To demonstrate the advantages of the proposed algorithm, various benchmarks have been selected with five different multiobjective optimization algorithms. The test results indicate that DMOEOA does exhibit better performances in these problems with a better balance between convergence and distribution. In addition, the new proposed algorithm is applied to the structural optimization of an elastic truss with the other five existing multiobjective optimization algorithms. The obtained results demonstrate that DMOEOA is not only an algorithm with good performance for benchmark problems but is also expected to have a wide application in real-world engineering optimization problems.

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