List of articles (by subject) Meta-heurestics
-
Open Access Article
1 - MOEICA: Enhanced multi-objective optimization based on imperialist competitive algorithm
امیرعلی نظری علی دیهیمیIn this paper, a multi-objective enhanced imperialist competitive algorithm (MOEICA) is presented. The main structures of the original ICA are employed while some novel approaches are also developed. Other than the non-dominated sorting and crowding distance methods whi MoreIn this paper, a multi-objective enhanced imperialist competitive algorithm (MOEICA) is presented. The main structures of the original ICA are employed while some novel approaches are also developed. Other than the non-dominated sorting and crowding distance methods which are used as the main tools for comparing and ranking solutions, an auxiliary comparison approach called fuzzy possession is also incorporated. This new provision enables more countries to participate in guiding the population towards different searching routs. Moreover the computational burden of the algorithm is abated by carrying out the hefty sorting process not at each iteration but at some predefined intervals. The frequency of which is controlled by on optional parameter. Furthermore, the recreation of empires and imperialists several times during the optimization progress, encourages better exploration and less chance to get trapped in local optima. The eligibility of the algorithm is tested on fifteen benchmark functions in terms of different performance metrics. The results through the comparison with NSGA-II and MOPSO shows that the MOEICA is a more effective and reliable multi-objective solver with being able to largely cover the true Pareto fronts (PFs) for the test functions applied in this article Manuscript profile -
Open Access Article
2 - Optimal Design of Open Channel Sections Using PSO Algorithm
محسن منادی میرعلی محمدی Hamed TaghizadehThis paper applies an evolutionary algorithm, the particle swarm optimization (PSO), to design the optimum open channel section. Depth, channel side slope and bottom width are considered as the variables for rectangular, triangular and trapezoidal channels, respectively MoreThis paper applies an evolutionary algorithm, the particle swarm optimization (PSO), to design the optimum open channel section. Depth, channel side slope and bottom width are considered as the variables for rectangular, triangular and trapezoidal channels, respectively. The objective function is minimizing the construction cost of the channel section. MATLAB software is used for programming and doing the optimization process. Manning’s uniform flow formula has been used as a constraint for the optimization model. The cost function is included the cost of earthwork, the increment in the cost of earthwork with the depth below the ground surface and the cost of lining. Simple functions of unit cost terms have been used to express the optimum values of section variables. The optimum section variables are obtained for the case of minimum area or minimum wetted perimeter problems. The results of this study showed that the PSO is a robust algorithm to compute the optimum section variables in open channel design. Manuscript profile -
Open Access Article
3 - Flying Squirrel Optimizer (FSO): A novel SI-based optimization algorithm for engineering problems
غلامرضا عزیزیان فرید میارنعیمی محسن راشکی ناصر شابختیThis paper provides a novel meta-heuristic optimization algorithm. The behaviors of flying squirrels in the nature are the main inspiration of this research. These behaviors include flying from tree to tree and walking on the ground or on a tree branch to find food. The MoreThis paper provides a novel meta-heuristic optimization algorithm. The behaviors of flying squirrels in the nature are the main inspiration of this research. These behaviors include flying from tree to tree and walking on the ground or on a tree branch to find food. They also contact each other with chirp or squeak. This algorithm is named flying squirrel optimizer (FSO). Two main theories of motion were used for the simulation of flying and walking of the flying squirrels and they are Lévy flight and normal random walk. FSO is also benchmarked on twelve mathematical functions and the answers are compared with MFO, PSO, GSA, BA, FPA, SMS, and FA. The results show that FSO can provide good results when compared with these well-known meta-heuristics approaches. Five classical engineering problems and a real issue in the field of dam engineering were employed to challenge the FSO abilities in solving engineering design problems. The results also show that the proposed FSO algorithm can be used on a wide range of problems with unknown search spaces. Manuscript profile -
Open Access Article
4 - Stochastic Facility Layout Planning Problem: A Metaheuristic and Case Study
Nima MoradiFacility layout is one of the most important Operations Management problems due to its direct impact on the financial performance of both private and public firms. Facility layout problem (FLP) with stochastic parameters, unequal area facilities, and grid system modelin MoreFacility layout is one of the most important Operations Management problems due to its direct impact on the financial performance of both private and public firms. Facility layout problem (FLP) with stochastic parameters, unequal area facilities, and grid system modeling is named GSUA-STFLP. This problem has not been worked in the literature so that to solve GSUA-STFLP is our main contribution. In this paper, we have first presented an integer nonlinear programming model which aims to minimize the cost of material handling. Then, a metaheuristic SA-based algorithm is proposed. Our proposed SA is able to generate feasible solutions by a local search operator to explore and exploit the solution space. Next, problems with different sizes besides the real case study have been solved. The computational results show the capability of the proposed SA to obtain the solutions with high quality in a short time. Manuscript profile -
Open Access Article
5 - An Ant-Colony Optimization Clustering Model for Cellular Automata Routing in Wireless Sensor Networks
عارف صفریHigh efficient routing is an important issue for the design of wireless sensor network (WSN) protocols to meet the severe hardware and resource constraints. This paper presents an inclusive evolutionary reinforcement method. The proposed approach is a combination of Cel MoreHigh efficient routing is an important issue for the design of wireless sensor network (WSN) protocols to meet the severe hardware and resource constraints. This paper presents an inclusive evolutionary reinforcement method. The proposed approach is a combination of Cellular Automata (CA) and Ant Colony Optimization (ACO) techniques in order to create collision-free trajectories for every agent of a team while their formation is kept unchallengeable. The method reacts with problem distribution changes and therefore can be used in dynamical or unknown environments, without the need of a priori knowledge of the space. The swarm of agents are divided into subgroups and all the desired trails are created with the combined use of a CA path finder and an ACO algorithm. In case of lack of pheromones, paths are created using the CA path finder. Compared to other methods, the proposed method can create accurate clustered, collision-free and reliable paths in real time with low complexity while the implemented system is completely autonomous. Manuscript profile -
Open Access Article
6 - A new method for solving of the Graph Coloring Problem based on a fuzzy logic and whale optimization algorithm
طاها مصطفایی فرزین مدرس خیابانی نیما جعفری نویمی پور بهروز دانشیانAbstract: In recent years, Graph Coloring Problem (GCP) is one of the main optimization problems from literature. Many real world problems interacting with changing environments can be modeled by dynamic graphs. Graph vertex coloring with a given number of colors is a w MoreAbstract: In recent years, Graph Coloring Problem (GCP) is one of the main optimization problems from literature. Many real world problems interacting with changing environments can be modeled by dynamic graphs. Graph vertex coloring with a given number of colors is a well-known and much-studied NP-hard problem. Meta-heuristic algorithms are a good choice to solve GCP because they are suitable for problems with NP-hard complexity. However, in many previously proposed algorithms, there are common problems such as runtime algorithm and low convergence of algorithm. Therefore, in this paper, we propose the Fuzzy Whale Optimization Algorithm (FWOA), a variety of basic Whale Optimization Algorithm (WOA), to improve runtime and convergence of algorithm in the GCP. Since WOA at first was introduced for solving continuous problem, we need a discrete WOA. Hence, to use FWOA to discrete search space, the standard arithmetic operators such as addition, subtraction and multiplication extant in FWOA encircling prey, exploitation phase and exploration phase operators based on distance’s theory needs to be redefined in the discrete space. Parameters p and r, are defined randomly in the WOA algorithm in FWOA algorithm defined as fuzzy and are selected in fuzzy tragedy. A set of graph coloring benchmark problems are solved and their performance are compared with some well-known heuristic search methods. Results illustrate that FWOA algorithm are the original focus of this work and in most cases success rate is nearly 100% and the runtime and convergence algorithm has been improved on some graphs. But as we have illustrated that comparison with other manners, we cannot deduce that our algorithm is the best in all instance of graphs. It can be said that a proposed algorithm is able to compete with other algorithms in this context. Obtained results approved the high performance of proposed method. Manuscript profile -
Open Access Article
7 - Iterative random search heuristic for the Single-Source Capacitated Multi-Facility Weber Problem with Setup Costs
سعید جهادیHere, we will study the Single-Source Capacitated Multi-Facility Weber Problem with Setup Costs (SSCMFWP-SC) to find location of certain numbers of facilities in continuous space so that demands by certain numbers of customers would be satisfied. This would be done in a MoreHere, we will study the Single-Source Capacitated Multi-Facility Weber Problem with Setup Costs (SSCMFWP-SC) to find location of certain numbers of facilities in continuous space so that demands by certain numbers of customers would be satisfied. This would be done in a way that total transportation cost between customers and facilities as well as total setup cost would be minimized. Facilities have limited capacity and each customer has to satisfy all of its demands just from one facility. Setup cost of facilities is variable and dependent on combination of machineries used by each facility. To solve the problem, Two versions of the proposed heuristic method named iterative random search will be presented in which local search method and exact solution method are used. proposed method has been tested on a dataset available in the literature and the obtained solutions compared to the best of them in the literatures. The results show extraordinary performance of recommended methods. Moreover, best available solutions in the literature have been improved and the best obtained solutions can be used as a comparison source in future studies. Manuscript profile -
Open Access Article
8 - Multi-objective firefly optimization algorithm for construction site layout planning
Abolfazl Ghadiri داود صداقت شایگان علی اصغر امیرکاردوستSafety importance on construction site layout plan is an essential requirement to improve construction project management. In previous studies the safety objective function is considered without risk factors analysis. Metaheuristics are widely used to solve construction MoreSafety importance on construction site layout plan is an essential requirement to improve construction project management. In previous studies the safety objective function is considered without risk factors analysis. Metaheuristics are widely used to solve construction site layout problems (CSLP). Firefly Algorithm (FA) is employed as multi-objective optimization method to design and optimize two safety objective functions and total cost. Safety objective functions (due to potential risks arising from hazardous sources and interaction flows) connecting temporary facilities by considering total cost reduction. A case study is presented to find out accuracy of the proposed model. Finally, the performance of two metaheuristic algorithms called Firefly Algorithm (FA) and Ant Colony Optimization (ACO) are compared in terms of their effectiveness in resolving a practical construction site layout problem. Results show that the FA performs better than the ACO Algorithm. Manuscript profile