Many real life optimization problems are multimodal. Evolutionary Algorithms (EA) have successfully been applied to solve these problems, but have the disadvantage that converge to only one optimum, even though there are many optima. We proposed a hybrid algorithm combining differential evolution (DE) with tabu search (TS) to solve these problems, allowing to find multiple solutions. The propos...

The speech signal enhancement is essential to obtain clean speech signal from noisy signal. For multimodal optimization, the natural-inspired algorithms such as Firefly Algorithm (FA) are better. The proposed algorithm contains preprocessing module, optimization module and spectral filtering module. Here, Loizou’s and Aurora databases are considered for signals. In this paper the Perceptional E...

Recent research on evolutionary multiobjective optimization has mainly focused on Pareto-fronts. However, we state that proper behavior of the utilized algorithms in decision/search space is necessary for obtaining good results if multimodal objective functions are concerned. Therefore, it makes sense to observe the development of Pareto-sets as well. We do so on a simple, configurable problem,...

Ben-Qiong Hu College of Information Management, Chengdu University of Technology, 610059, China Abstract To find all extreme points of multimodal functions is called extremum problem, which is a well known difficult issue in optimization fields. Applying ant colony optimization (ACO) to solve this problem is rarely reported. The method of applying ACO to solve extremum problem is explored in th...

In this paper, we study the traffic optimization problem for a heterogeneous traffic network by considering utility functions of data and voice traffic. We also study the performance of multimodal networks, where other modes of communication like wires, infrared links may operate in addition to wireless nodes to improve the performance of the wireless network. We assume the presence of an addit...

Recent research on evolutionary multiobjective optimization has mainly focused on Pareto-fronts. However, we state that proper behavior of the utilized algorithms in decision/search space is necessary for obtaining good results if multimodal objective functions are concerned. Therefore, it makes sense to observe the development of Pareto-sets as well. We do so on a simple, configurable problem,...

Recent research on evolutionary multiobjective optimization has mainly focused on Pareto-fronts. However, we state that proper behavior of the utilized algorithms in decision/search space is necessary for obtaining good results if multimodal objective functions are concerned. Therefore, it makes sense to observe the development of Pareto-sets as well. We do so on a simple, configurable problem,...

In this paper we present an empirical, comparative performance, analysis of fourteen Differential Evolution (DE) variants on different classes of unconstrained global optimization benchmark problems. This analysis has been undertaken, with an objective, to compare and to identify competitive DE variants which perform reasonably well on problems with different features. Towards this, fourteen va...

The problem of finding a global optimum of an unconstrained multimodal function has been the subject of intensive study in recent years, giving rise to valuable advances in solution methods. We examine this problem within the framework of adaptive memory programming (AMP), focusing particularly on AMP strategies that derive from an integration of Scatter Search and Tabu Search. Computational co...

This paper presents a novel explicit exploration information exchange mechanism for niche technique. In this framework, the whole population is divided into many sub-populations. The different sub-population communicates with each other. One sub-population exploration area does not be explored by others. Based on this framework, a multi-sub-swarm particle swarm optimization (MSSPSO) algorithm i...