Multi-Objective Equivalent Random Search

2006 ◽  
pp. 463-472 ◽  
Author(s):  
Evan J. Hughes
Keyword(s):  
Random Search ◽  
Multi Objective

Swarm Intelligence for Multi-Objective Optimization in Engineering Design

2018 ◽  
pp. 239-250 ◽  
Author(s):  
Janga Reddy Manne
Keyword(s):  
Swarm Intelligence ◽  
Engineering Design ◽  
Random Search ◽  
Search Space ◽  
Population Based ◽  
Design Problems ◽  
Multi Objective ◽  
Hard Constraints ◽  
Decision Variables ◽  
Engineering Design Problems

Most of the engineering design problems are intrinsically complex and difficult to solve, because of diverse solution search space, complex functions, continuous and discrete nature of decision variables, multiple objectives and hard constraints. Swarm intelligence (SI) algorithms are becoming popular in dealing with these kind of complexities. The SI algorithms being population based random search techniques, use heuristics inspired from nature to enable effective exploration of optimal solutions to complex engineering problems. The SI algorithms derived based on principles of co-operative group intelligence and collective behavior of self-organized systems. This chapter presents key principles of multi-optimization, and swarm optimization for solving multi-objective engineering design problems with illustration through few examples.

scholarly journals 2DOF multi-objective optimal tuning of disturbance reject fractional order PIDA controllers according to improved consensus oriented random search method

2020 ◽  
Vol 25 ◽  
pp. 159-170
Author(s):  
Necati Ozbey ◽  
Celaleddin Yeroglu ◽  
Baris Baykant Alagoz ◽  
Norbert Herencsar ◽  
Aslihan Kartci ◽  
...  
Keyword(s):  
Fractional Order ◽  
Random Search ◽  
Search Method ◽  
Optimal Tuning ◽  
Multi Objective ◽  
Random Search Method

517 A multi-objective optimization with random search

2008 ◽  
Vol 2008.21 (0) ◽  
pp. 237-238
Author(s):  
Masataka Urago ◽  
Hidekazu Nishimura ◽  
Yoshiaki Ohkami
Keyword(s):  
Random Search ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals On the Hardness of Offline Multi-objective Optimization

2007 ◽  
Vol 15 (4) ◽  
pp. 475-491 ◽  
Author(s):  
Olivier Teytaud
Keyword(s):  
Evolutionary Algorithms ◽  
Convergence Rate ◽  
Random Search ◽  
Computational Cost ◽  
Multiobjective Evolutionary Algorithms ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Insight Into ◽  
Better Than

It has been empirically established that multiobjective evolutionary algorithms do not scale well with the number of conflicting objectives. This paper shows that the convergence rate of all comparison-based multi-objective algorithms, for the Hausdorff distance, is not much better than the convergence rate of the random search under certain conditions. The number of objectives must be very moderate and the framework should hold the following assumptions: the objectives are conflicting and the computational cost is lower bounded by the number of comparisons is a good model. Our conclusions are: (i) the number of conflicting objectives is relevant (ii) the criteria based on comparisons with random-search for multi-objective optimization is also relevant (iii) having more than 3-objectives optimization is very hard. Furthermore, we provide some insight into cross-over operators.

Swarm Intelligence for Multi-Objective Optimization in Engineering Design

2019 ◽  
pp. 180-194
Author(s):  
Janga Reddy Manne
Keyword(s):  
Swarm Intelligence ◽  
Engineering Design ◽  
Random Search ◽  
Search Space ◽  
Population Based ◽  
Design Problems ◽  
Multi Objective ◽  
Hard Constraints ◽  
Decision Variables ◽  
Engineering Design Problems

Most of the engineering design problems are intrinsically complex and difficult to solve because of diverse solution search space, complex functions, continuous and discrete nature of decision variables, multiple objectives, and hard constraints. Swarm intelligence (SI) algorithms are becoming popular in dealing with these complexities. The SI algorithms, being population-based random search techniques, use heuristics inspired from nature to enable effective exploration of optimal solutions to complex engineering problems. The SI algorithms derived from principles of cooperative group intelligence and collective behavior of self-organized systems. This chapter presents key principles of multi-optimization and swarm optimization for solving multi-objective engineering design problems with illustration through a few examples.

scholarly journals From Single- to Multi-Objective Auto-Tuning of Programs: Advantages and Implications

2014 ◽  
Vol 22 (4) ◽  
pp. 285-297 ◽  
Author(s):  
Juan Durillo ◽  
Thomas Fahringer
Keyword(s):  
Random Search ◽  
Search Space ◽  
Computing System ◽  
Automatic Tuning ◽  
Evolutionary Search ◽  
Multi Objective ◽  
Hardware Architectures ◽  
Auto Tuning ◽  
Data Sensitivity ◽  
Single Objective

Automatic tuning (auto-tuning) of software has emerged in recent years as a promising method that tries to automatically adapt the behaviour of a program to attain different performance objectives on a given computing system. This method is gaining momentum due to the increasing complexity of modern multicore-based hardware architectures. Many solutions to auto-tuning have been explored ranging from simple random search to more sophisticate methods like machine learning or evolutionary search. To this day, it is still unclear whether these approaches are general enough to encompass all the complexities of the problem (e.g. search space, parameters influencing the search space, input data sensitivity, etc.), or which approach is best suited for a given problem. Furthermore, the growing interest in auto-tuning a program for several objectives is increasing this confusion even further. The goal of this paper is to formally describe the problem addressed by auto-tuning programs and review existing solutions highlighting the advantages and drawbacks of different techniques for single-objective as well as multi-objective auto-tuning approaches.

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