Cuckoo search (CS) is a relatively new algorithm, developed by Yang and Deb in 2009, and CS is efficient in solving global optimization problems. In this paper, we review the fundamental ideas of cuckoo search and the latest developments as well as its applications. We analyze the algorithm and gain insight into its search mechanisms and find out why it is efficient. We also discuss the essence of algorithms and its link to self-organizing systems, and finally we propose some important topics for further research.

Cuckoo Search: Recent Advances and Applications

Cuckoo search (CS) is a relatively new algorithm, developed by Yang and Deb in 2009, and the same has been found to be efficient in solving global optimization problems. In this paper, we review the fundamental ideas of cuckoo search and the latest developments as well as its applications. We analyze the algorithm and gain insight into its search mechanisms and find out why it is efficient. We also discuss the essence of algorithms and its link to self-organizing systems, and finally, we propose some important topics for further research.

Cuckoo search: recent advances and applications

This paper reviews recent studies on the Particle Swarm Optimization PSO algorithm. The review has been focused on high impact recent articles that have analyzed and/or modified PSO algorithms. This paper also presents some potential areas for future study.

Book review: particle swarm optimization for single objective continuous space problems: A review

Optimal reactive power dispatch problem in power systems has thrown a growing influence on secure and economical operation of power systems. However, this issue is well known as a nonlinear, multimodal and mixed-variable problem. In the last decades, computation intelligence-based techniques, such as genetic algorithms (GAs), differential evolution (DE) algorithms and particle swarm optimization (PSO) algorithms, etc., have often been used for this aim. In this work, a seeker optimization algorithm (SOA)-based reactive power dispatch method is proposed. The SOA is based on the concept of simulating the act of human searching, where the search direction is based on the empirical gradient by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple Fuzzy rule. In this study, the algorithm's performance is evaluated on benchmark function optimization. Then, the SOA is applied to optimal reactive power dispatch on standard IEEE 57- and 118-bus power systems, and compared with conventional nonlinear programming method, two versions of GAs, three versions of DE algorithms and four versions of PSO algorithms. The simulation results show that the proposed approach is superior to the other listed algorithms and can be efficiently used for optimal reactive power dispatch.

Seeker Optimization Algorithm for Optimal Reactive Power Dispatch

Quantum-behaved particle swarm optimization (QPSO), motivated by concepts from quantum mechanics and particle swarm optimization (PSO), is a probabilistic optimization algorithm belonging to the bare-bones PSO family. Although it has been shown to perform well in finding the optimal solutions for many optimization problems, there has so far been little analysis on how it works in detail. This paper presents a comprehensive analysis of the QPSO algorithm. In the theoretical analysis, we analyze the behavior of a single particle in QPSO in terms of probability measure. Since the particle's behavior is influenced by the contraction-expansion (CE) coefficient, which is the most important parameter of the algorithm, the goal of the theoretical analysis is to find out the upper bound of the CE coefficient, within which the value of the CE coefficient selected can guarantee the convergence or boundedness of the particle's position. In the experimental analysis, the theoretical results are first validated by stochastic simulations for the particle's behavior. Then, based on the derived upper bound of the CE coefficient, we perform empirical studies on a suite of well-known benchmark functions to show how to control and select the value of the CE coefficient, in order to obtain generally good algorithmic performance in real world applications. Finally, a further performance comparison between QPSO and other variants of PSO on the benchmarks is made to show the efficiency of the QPSO algorithm with the proposed parameter control and selection methods.

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Quantum-behaved particle swarm optimization: Analysis of individual particle behavior and parameter selection

Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm

Particle swarm optimization (PSO) has shown to be an efficient, robust and simple optimization algorithm, and has been successfully applied to many different kinds of problems. But it is still an open problem that why PSO can be successful. Most of current PSO studies are empirical, with only a few theoretical analyses, and these theoretical studies concentrate mainly on simplified PSO systems, discarding randomness. In order to improve the understanding of real stochastic PSO algorithm, this paper presents a formal stochastic analysis of the stochastic PSO algorithm, which involves with randomness. The stochastic properties of particle trajectories in stagnation phase are studied in details

Stagnation Analysis in Particle Swarm Optimization

Interactions are a core part of all Multi-Agent Systems, which have tight correlation to the topology of the distribution of the agents. Following the work of Lor and Szeto [1,2], we propose two simple models of negotiating agent with bids and offers that depend on the resources as well as association of the agents on a soap froth topology. We present a study of two groups of agents, represented by two colors, (red and blue) and the color switching dynamics of the negotiating autonomous agents in a soap froth world. The model of interaction between neighboring agents is defined by two parameters: the threshold of color switching and the weight representing the strength of cooperation. By changing these parameters, we observe evidences for two phase transitions: the first one occurs when the cellular network of agents transits from an ordered phase to a disordered phase, and the second one occurs when the network return from the disordered phase back to an ordered phase. Our results are indicative of the importance of cooperation of agents in a competing world.

Ming Jiang

Papers

Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm

Stagnation Analysis in Particle Swarm Optimization

Dynamics of Negotiating Agent in a Soap Froth World