Biologically-Inspired Optimisation Methods (original) (raw)
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52 Bioinspired Optimization Methods and Their Applications
2015
Since the mid-fties evolutionary algorithms (EAs) have been used in different optimization problems. In the last years their use was extended to the demand-ing eld of multi-objective optimization. For this expansion, EAs themselves had to evolve to more complex forms. The question is whether an algorithm that is adapted to work well with multiple-objectives is still capable to handle single-objective optimization problems. In this paper we present a new EA for multi-objective optimization called MOGA-II. We test it on noisy single-objective problems and compare its performance with two algorithms for single-objective optimization. The results show that MOGA-II is a robust algorithm that can ef ciently solve a palette of different optimization problems.
The good of the many outweighs the good of the one: evolutionary multi-objective optimization
2003
Abstract. We dwell in largely non-technical terms on the essential differences between single-objective optimization and multiple-objective optimization. We argue in particular that single-objective approaches to real-world problems are almost invariably simplifications of the real-problem which make many ideal solutions unreachable to the optimization method. We promote the use of multi-objective optimization methods, particularly those arising from the evolutionary computation community.
Evolutionary Multi-Objective Algorithms
Real-World Applications of Genetic Algorithms, 2012
Real-World Applications of Genetic Algorithms 54 approach and a group of experimental results, as well as some conclusions and future work. Finally, the last section of this chapter is a brief reflection on the future of multi-objective optimization research. On it, we capture some concerns and issues that are relevant to the development of this area. 2. Multi-objective optimization Optimization in both mathematics and computing, refers to the determination of one or more feasible solutions that corresponds to an extreme value (maximum or minimum), according to one or more objective functions. To find the extreme solutions of one or more objective functions can be applied in a wide range of practical situations, such as to minimize the manufacturing cost of a product, to maximize profit, to reduce uncertainty, and so on. The principles and methods of optimization are used in solving quantitative problems in disciplines such as physics, biology, engineering, economics, and others. The simplest optimization problems involve functions of a single variable and can be solved by differential calculus. When researchers work with optimization, we could find two main types: mono-objective optimization and multi-objective optimization (MOO), depending on the number of optimization functions. The optimization can be subject to one or several constraints. The constraints are conditions that limit the selection of the values variables can take. This area has been approached for different techniques and methods.
Evolutionary Population Dynamics and Multi-Objective Optimisation Problems
Theory and Practice, 2008
Problems for which many objective functions are to be simultaneously optimised are widely encountered in science and industry. These multiobjective problems have also been the subject of intensive investigation and development recently for metaheuristic search algorithms such as ant colony optimisation, particle swarm optimisation and extremal optimisation. In this chapter, a unifying framework called evolutionary programming dynamics (EPD) is examined. Using underlying concepts of self organised criticality and evolutionary programming, it can be applied to many optimisation algorithms as a controlling metaheuristic, to improve performance and results. We show this to be effective for both continuous and combinatorial problems.
Multi-Objective Evolutionary Algorithms
Encyclopedia of Artificial Intelligence
Real world optimization problems are often too complex to be solved through analytical means. Evolutionary algorithms, a class of algorithms that borrow paradigms from nature, are particularly well suited to address such problems. These algorithms are stochastic methods of optimization that have become immensely popular recently, because they are derivative-free methods, are not as prone to getting trapped in local minima (as they are population based), and are shown to work well for many complex optimization problems. Although evolutionary algorithms have conventionally focussed on optimizing single objective functions, most practical problems in engineering are inherently multi-objective in nature. Multi-objective evolutionary optimization is a relatively new, and rapidly expanding area of research in evolutionary computation that looks at ways to address these problems. In this chapter, we provide an overview of some of the most significant issues in multi-objective optimization ...
2012
The emergence of different metaheuristics and their new variants in recent years has made the definition of the term Evolutionary Algorithms
On the effect of populations in evolutionary multi-objective optimisation
Evolutionary Computation, 2010
Multi-objective evolutionary algorithms (MOEAs) have become increasingly popular as multi-objective problem solving techniques. Most studies of MOEAs are empirical. Only recently, a few theoretical results have appeared. It is acknowledged that more theoretical research is needed. An important open problem is to understand the role of populations in MOEAs. We present a simple bi-objective problem which emphasizes when populations are needed. Rigorous runtime analysis point out an exponential runtime gap between a population-based algorithm (SEMO) and several single individual-based algorithms on this problem. This means that among the algorithms considered, only the populationbased MOEA is successful and all other algorithms fail.
Simulated Evolution under Multiple Criteria Conditions Revisited
Lecture Notes in Computer Science, 2008
Evolutionary Algorithms (EAs) as one important subdomain of Computational Intelligence (CI) have conquered the field of experimental as well as difficult numerical optimization despite the lack of addresses of welcome half a century ago. Meanwhile, they go without saying into the toolboxes of most practitioners who have to solve real-world problems. And an overwhelming number of theoretical results underpin at least parts of the practice. More recently, even vector optimization problems can be tackled by means of specialized EAs. These multiobjective evolutionary algorithms (MOEAs or EMOAs) help decision makers to reduce the number of design possibilities to the subsets that make the best of the situation in case of conflicting objectives. This article briefly describes the problem setting, the most important solution approaches, and the challenges that still lie ahead in their improvement. Most sophisticated algorithms in this domain have somehow lost their character of mimicking natural mechanisms found in organic evolution. That is why a couple of more bio-inspired aspects are mentioned in the second part of this contribution that may help to diversify further research and practice in multiobjective optimization (MOO) without forgetting to foster the interdisciplinary dialogue with natural scientists.
I-EMO: An Interactive Evolutionary Multi-objective Optimization Tool
Lecture Notes in Computer Science, 2005
With the advent of efficient techniques for multi-objective evolutionary optimization (EMO), real-world search and optimization problems are being increasingly solved for multiple conflicting objectives. During the past decade, most emphasis has been spent on finding the complete Pareto-optimal set, although EMO researchers were always aware of the importance of procedures which would help choose one particular solution from the Pareto-optimal set for implementation. This is also one of the main issues on which the classical and EMO philosophies are divided on. In this paper, we address this long-standing issue and suggest an interactive EMO procedure which, for the first time, will involve a decision-maker in the evolutionary optimization process and help choose a single solution at the end. This study is the culmination of many year's of research on EMO and would hopefully encourage both practitioners and researchers to pay more attention in viewing the multi-objective optimization as an aggregate task of optimization and decision-making.