scholarly journals A HYBRID OPTIMIZATION APPROACH FOR COMPLEX NONLINEAR OBJECTIVE FUNCTIONS

2018 ◽  
pp. 102-112
Author(s):  
Samuel O. Obadan ◽  
Zenghui Wang
Keyword(s):  
Objective Function ◽  
Hybrid Algorithm ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Search Space ◽  
Bench Mark ◽  
Optimization Approach ◽  
Stochastic Algorithm ◽  
Objective Functions ◽  
Function Calls

With respect to the ‘no free launch’ theorem, no single algorithm has a better performance when tested against a completely stochastic algorithm on all objective functions. Consequently, choosing the best algorithm for a particular problem is often more of an art than science. The complexity of an objective function can be determined by certain features such as the modality, the basins, the valleys, the separability, and the dimensionality of the objective function. While the separability and modality contribute to the complexity of the function, the dimensionality and domain range increases the function’s search space exponentially. In this paper, the authors analyze the algorithmic constructs of Simulated Annealing (SA), Cuckoo-search (CK), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) along with two hybrid paradigms. In addition, an extensive comparative study was conducted using 30 standard bench mark functions to demonstrate how an ingenious hybrid algorithm could significantly shorten the amount of function calls (generations) needed to attain the optimal or rather near optimal solution for almost any complex objective function. Results from empirical analysis unveil the precision, robustness and success of the hybrid algorithm (without compromising run-time complexity) over its counterparts.

scholarly journals An evolutionary-based optimization algorithm for truss sizing design

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.

scholarly journals A two-stage evolutionary optimization approach for an irrigation system design

2016 ◽  
Vol 19 (1) ◽  
pp. 115-122 ◽  
Author(s):  
Milan Cisty ◽  
Zbynek Bajtek ◽  
Lubomir Celar
Keyword(s):  
Water Distribution ◽  
Irrigation System ◽  
Optimal Solution ◽  
Water Distribution Network ◽  
Search Space ◽  
Optimization Method ◽  
Global Optimum ◽  
Optimization Approach ◽  
Nsga Ii ◽  
Irrigation Network

In this work, an optimal design of a water distribution network is proposed for large irrigation networks. The proposed approach is built upon an existing optimization method (NSGA-II), but the authors are proposing its effective application in a new two-step optimization process. The aim of the paper is to demonstrate that not only is the choice of method important for obtaining good optimization results, but also how that method is applied. The proposed methodology utilizes as its most important feature the ensemble approach, in which more optimization runs cooperate and are used together. The authors assume that the main problem in finding the optimal solution for a water distribution optimization problem is the very large size of the search space in which the optimal solution should be found. In the proposed method, a reduction of the search space is suggested, so the final solution is thus easier to find and offers greater guarantees of accuracy (closeness to the global optimum). The method has been successfully tested on a large benchmark irrigation network.

scholarly journals AN ALGORITHM FOR OBTAINING THE OPTIMAL GUARANTEED RESULT FOR OBJECTS WITH EXTREME CONDITIONS

2020 ◽  
Vol 72 (4) ◽  
pp. 34-40
Author(s):  
А.Е. Ismayilov ◽  
◽  
Zh. Kozhamkulova ◽  
M. Serikuly ◽  
◽  
...  
Keyword(s):  
Information Technology ◽  
Objective Function ◽  
Control Strategy ◽  
Rational Solution ◽  
Optimal Solution ◽  
Analytical Form ◽  
Extreme Conditions ◽  
Objective Functions ◽  
Existence Of A Solution ◽  
Multicriteria Problem

In this paper, an algorithm for obtaining the optimal guaranteed result for objects with extreme states is developed, based on the establishment of the function and functionals of an analytical form. The methodology for choosing a control strategy for an object with the best guaranteed result is described. The essence of the technique is to find an optimal solution to a multicriteria problem that maximizes the values of all functions. At the same time, the existence of a solution that literally maximizes all objective functions is a rare exception. The problem of obtaining a guaranteed result and the study of the possibility of its improvement, as well as the problem of choosing a rational solution were considered. It has been established that a strategy that has an efficiency estimate in a given operation equal to the best (largest) guaranteed result is the optimal guaranteeing strategy. The objective function has been determined, and an optimally guaranteed strategy has been undertaken for the management of biotechnological industries using information technology.

Diagnostic Strategy Optimization Based on Predatory Search Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 2441-2444
Author(s):  
Jin Luo ◽  
Qi Bin Deng ◽  
Chen Meng
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Global Search ◽  
State Probability ◽  
Diagnostic Strategy ◽  
Local Optimal Solution ◽  
Predatory Search Algorithm

With respect to the inherent NP-hard complexity of Optimization of testability diagnostic strategy problem, a predatory search algorithm simulating animal predatory strategies was designed. This algorithm adopted the gross test expense including state probability, isolation matrix and test expense as its objective function, defined local and global search by the restriction value of search space based on two points exchange, and realized the conversion between local and global search by adjusting the restriction value of search space. It had better ability to conduct local search and jump out of local optimal solution simultaneously, and provided a better resolution for the optimization of testability diagnostic strategy.

Optimization of Well Placement

1999 ◽  
Vol 122 (2) ◽  
pp. 64-70 ◽  
Author(s):  
Baris Guyaguler ◽  
Roland Horne
Keyword(s):  
Hybrid Algorithm ◽  
Single Point ◽  
Optimization Technique ◽  
Search Space ◽  
Complex Problem ◽  
Production Optimization ◽  
Optimal Placement ◽  
Optimization Approach ◽  
Injection Wells ◽  
Well Placement

Optimal placement of oil, gas or water wells is a complex problem that depends on reservoir and fluid properties, well and surface equipment specifications, as well as economic parameters. An optimization approach that enables the evaluation of all these information is presented. A hybrid of the genetic algorithm (GA) forms the basis of the optimization technique. GA operators such as uniform, single-point, two-point crossover, uniform mutation, elitism, tournament and fitness scaling were used. An additional operator that employs kriging is proposed. The GA was hybridized with the polytope algorithm, which makes use of the trends in the search space. The hybrid algorithm was tested on a set of mathematical functions with different characteristics in order to determine the performance sensitivity to GA operators and hybridization. Simple test cases of oil production optimization on 16×16 simulation grids with known optimum well locations were carried out to verify the hybrid GA results. Next, runs were carried out for a 32×32 problem. The locations of a production and injection well were optimized in the case of three existing producers. Exhaustive runs were carried out for these cases to determine the effects of the operators, hybridization and the population size on the performance of the algorithm for well placement problems. Subsequently, the optimum configuration of two injection wells were determined with two existing producers in the field. It was observed that the hybrid algorithm is able to reduce the required number of simulations substantially over simple GA. [S0195-0738(00)00502-1]

A New Hybrid Binary-Real Coded Cuckoo Search and Tabu Search Algorithm for Solving the Unit-Commitment Problem

2021 ◽  
Vol 10 (2) ◽  
pp. 104-119
Author(s):  
Amel Terki ◽  
Hamid Boubertakh
Keyword(s):  
Tabu Search ◽  
Local Search ◽  
Unit Commitment ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Optimization Techniques ◽  
Optimization Approach ◽  
Unit Commitment Problem ◽  
Local Optima

This paper proposes a new intelligent optimization approach to deal with the unit commitment (UC) problem by finding the optimal on/off states strategy of the units under the system constraints. The proposed method is a hybridization of the cuckoo search (CS) and the tabu search (TS) optimization techniques. The former is distinguished by its efficient global exploration mechanism, namely the levy flights, and the latter is a successful local search method. For this sake, a binary code is used for the status of units in the scheduled time horizon, and a real code is used to determine the generated power by the committed units. The proposed hybrid CS and TS (CS-TS) algorithm is used to solve the UC problem such that the CS guarantees the exploration of the whole search space, while the TS algorithm deals with the local search in order to avoid the premature convergence and prevent from trapping into local optima. The proposed method is applied to the IEEE standard systems of different scales ranging from 10 to 100 units. The results show clearly that the proposed method gives better quality solutions than the existing methods.

Optimal Location of Robot Base with Respect to the Application Positions by Using Proper Neural-Network Method

2015 ◽  
Vol 772 ◽  
pp. 482-487
Author(s):  
Liviu Ciupitu ◽  
Adrian Olaru
Keyword(s):  
Objective Function ◽  
Automotive Industry ◽  
Optimal Solution ◽  
Optimal Location ◽  
Minimum Time ◽  
Objective Functions ◽  
Working Space ◽  
Initial Task ◽  
Network Method ◽  
The Subject

Usual the location of robot base with respect to positions (configurations) of an application that the robot must reach is choose in such a way that the application points to be into working space of robot by avoiding the obstacles. Or the application is build from the very first beginning in such a way that all application points to be in the working space of robot because usual the robot base is fixed to the ground. But this is not the optimal solution with respect to an objective function which represents for example minimum time of motion during a cycle, or minimum consumption of energy, or maximum precision, or combination of these. Some objective functions could results from the specificity of the application like is the case of casting of forging where the accumulation of heat for example could be one of the optimization criteria. For example in the automotive industry the owners prefer to replace the whole robotized line when the product is changed instead of reprogramming robots because the prices of robots is decreasing and the price of reprogramming is increasing. For such a situation the placing of robot base in an optimum location from the very first beginning so that the time or/and energy consumption to be minimum is an essential initial task, especially for large series productions. The proposed paper is dealing with the subject of moving the base of robot with respect to the application points so that an objective function representing the minimum time of motion during a cycle to be fulfilled.

A Pareto-Optimal Solution for a Multi-Objective Scheduling Problem with Periodic Maintenance Requirement

2012 ◽  
Vol 3 (2) ◽  
pp. 24-45 ◽  
Author(s):  
Deniz Mungan ◽  
Junfang Yu ◽  
Bhaba R. Sarker ◽  
Mohammad Anwar Rahman
Keyword(s):  
Objective Function ◽  
Single Machine ◽  
Pareto Optimality ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Neighborhood Search ◽  
Pareto Optimal ◽  
Scheduling Problem ◽  
Objective Functions ◽  
Periodic Maintenance

A Pareto-optimal solution is developed in this paper for a scheduling problem on a single machine with periodic maintenance and non-preemptive jobs. Most of the scheduling problems address only one objective function, while in the real world, such problems are always associated with more than one objective. In this paper, both multi-objective functions and multi-maintenance periods are considered for a machine scheduling problem. To avoid complexities, multiple objective functions are consolidated and transformed into a single objective function after they are weighted and assigned proper weighting factors. In addition, periodic maintenance schedules are also considered in the model. The objective of the model addressed is to minimize the weighted function of the total job flow time, the maximum tardiness, and the machine idle time in a single machine problem with periodic maintenance and non-preemptive jobs. An algorithm is developed to solve this multiple criterion problem and to construct the Pareto-set. The parametric analysis of the trade-offs of all solutions with all possible weighted combination of the criteria is performed. A neighborhood search heuristic is also developed. Results are provided to explore the best schedule among all the Pareto-optimality sets and to compare the result of the modified Pareto-optimality algorithm with the result of the neighborhood search heuristic.

scholarly journals An Improved Hybrid Algorithm for Optimizing the Parameters of Hidden Markov Models

2021 ◽  
pp. 63-73
Author(s):  
Abukari Abdul Aziz Danaa ◽  
Mohammed Ibrahim Daabo ◽  
Alhassan Abdul-Barik
Keyword(s):  
Hidden Markov Models ◽  
Hybrid Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Real Life ◽  
Optimal Solution ◽  
Mathematical Structure ◽  
Search Space ◽  
Wide Range ◽  
Local Optimal Solution

Hidden Markov Models (HMMs) have become increasingly popular in the last several years due to the fact that, the models are very rich in mathematical structure and hence can form the theoretical basis for use in a wide range of applications. Various algorithms have been proposed in literature for optimizing the parameters of these models to make them applicable in real-life. However, the performance of these algorithms has remained computationally challenging largely due to slow/premature convergence and their sensitivity to preliminary estimates. In this paper, a hybrid algorithm comprising the Particle Swarm Optimization (PSO), Baum-Welch (BW), and Genetic Algorithms (GA) is proposed and implemented for optimizing the parameters of HMMs. The algorithm not only overcomes the shortcomings of the slow convergence speed of the PSO but also helps the BW escape from local optimal solution whilst improving the performance of GA despite the increase in the search space. Detailed experimental results demonstrates the effectiveness of our proposed approach when compared to other techniques available in literature.

scholarly journals Optimizing Constraint Solving via Dynamic Programming

2019 ◽  
Author(s):  
Shu Lin ◽  
Na Meng ◽  
Wenxin Li
Keyword(s):  
Dynamic Programming ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Search Space ◽  
Constraint Solving ◽  
Constraint Optimization ◽  
Objective Functions ◽  
Constraint Solver ◽  
Constraint Optimization Problems ◽  
Finite Domains

Constraint optimization problems (COP) on finite domains are typically solved via search. Many problems (e.g., 0-1 knapsack) involve redundant search, making a general constraint solver revisit the same subproblems again and again. Existing approaches use caching, symmetry breaking, subproblem dominance, or search with decomposition to prune the search space of constraint problems. In this paper we present a different approach--DPSolver--which uses dynamic programming (DP) to efficiently solve certain types of constraint optimization problems (COPs). Given a COP modeled with MiniZinc, DPSolver first analyzes the model to decide whether the problem is efficiently solvable with DP. If so, DPSolver refactors the constraints and objective functions to model the problem as a DP problem. Finally, DPSolver feeds the refactored model to Gecode--a widely used constraint solver--for the optimal solution. Our evaluation shows that DPSolver significantly improves the performance of constraint solving.

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