Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

http://romisatriawahono.net/lecture/rm/survey/softcomputing/Boussaid%20-%20Optimization%20Metaheuristics%20-%202013.pdf

A survey on optimization metaheuristics

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

This paper presents a comprehensive learning particle swarm optimization algorithm with enhanced exploration and exploitation, named as “heterogeneous comprehensive learning particle swarm optimization” (HCLPSO). In this algorithm, the swarm population is divided into two subpopulations. Each subpopulation is assigned to focus solely on either exploration or exploitation. Comprehensive learning (CL) strategy is used to generate the exemplars for both subpopulations. In the exploration-subpopulation, the exemplars are generated by using personal best experiences of the particles in the exploration-subpopulation itself. In the exploitation-subpopulation, the personal best experiences of the entire swarm population are used to generate the exemplars. As the exploration-subpopulation does not learn from any particles in the exploitation-subpopulation, the diversity in the exploration-subpopulation can be retained even if the exploitation-subpopulation converges prematurely. The heterogeneous comprehensive learning particle swarm optimization algorithm is tested on shifted and rotated benchmark problems and compared with other recent particle swarm optimization algorithms to demonstrate superior performance of the proposed algorithm over other particle swarm optimization variants.

Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation

Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.

/pdf/a-self-learning-particle-swarm-optimizer-for-global-11pdihwpgn.pdf

A Self-Learning Particle Swarm Optimizer for Global Optimization Problems

https://academic.csuohio.edu/simond/pubs/LBBO.pdf

Linearized biogeography-based optimization with re-initialization and local search

This paper presents a study of the performance of TRIBES, an adaptive particle swarm optimization algorithm. Particle Swarm Optimization (PSO) is a biologically-inspired optimization method. Recently, researchers have used it effectively in solving various optimization problems. However, like most optimization heuristics, PSO suffers from the drawback of being greatly influenced by the selection of its parameter values. Thus, the common belief is that the performance of a PSO algorithm is directly related to the tuning of such parameters. Usually, such tuning is a lengthy, time consuming and delicate process. A new adaptive PSO algorithm called TRIBES avoids manual tuning by defining adaptation rules which aim at automatically changing the particles’ behaviors as well as the topology of the swarm. In TRIBES, the topology is changed according to the swarm behavior and the strategies of displacement are chosen according to the performances of the particles. A comparative study carried out on a large set of benchmark functions shows that the performance of TRIBES is quite competitive compared to most other similar PSO algorithms that need manual tuning of parameters. The performance evaluation of TRIBES follows the testing procedure introduced during the 2005 IEEE Conference on Evolutionary Computation. The main objective of the present paper is to perform a global study of the behavior of TRIBES under several conditions, in order to determine strengths and drawbacks of this adaptive algorithm.

Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm

Particle swarm optimization (PSO) is characterized by a fast convergence, which can lead the algorithms of this class to stagnate in local optima. In this paper, a variant of the standard PSO algorithm is presented, called PSO-2S, based on several initializations in different zones of the search space, using charged particles. This algorithm uses two kinds of swarms, a main one that gathers the best particles of auxiliary ones, initialized several times. The auxiliary swarms are initialized in different areas, then an electrostatic repulsion heuristic is applied in each area to increase its diversity. We analyse the performance of the proposed approach on a testbed made of unimodal and multimodal test functions with and without coordinate rotation and shift. The Lennard-Jones potential problem is also used. The proposed algorithm is compared to several other PSO algorithms on this benchmark. The obtained results show the efficiency of the proposed algorithm.

A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization

Currently, two very similar versions of PSO are available that could be called "standard". While it is easy to merge them, their common drawbacks still remain. Therefore, in this paper, the author goes beyond simple merging by suggesting simple yet robust changes and solving a few well-known, common problems, while retaining the classical structure. The results can be proposed to the "swarmer community" as a new standard.

Beyond Standard Particle Swarm Optimisation

This paper presents MO-TRIBES, an adaptive multiobjective Particle Swarm Optimization (PSO) algorithm. Metaheuristics have the drawback of being very dependent on their parameter values. Then, performances are strongly related to the fitting of parameters. Usually, such tuning is a lengthy, time consuming and delicate process. The aim of this paper is to present and to evaluate MO-TRIBES, which is an adaptive algorithm, designed for multiobjective optimization, allowing to avoid the parameter fitting step. A global description of TRIBES and a comparison with other algorithms are provided. Using an adaptive algorithm means that adaptation rules must be defined. Swarm's structure and strategies of displacement of the particles are modified during the process according to the tribes behaviors. The choice of the final solutions is made using the Pareto dominance criterion. Rules based on crowding distance have been incorporated in order to maintain diversity along the Pareto Front. Preliminary simulations are provided and compared with the best known algorithms. These results show that MO-TRIBES is a promising alternative to tackle multiobjective problems without the constraint of parameter fitting.

Maurice Clerc

Papers

Linearized biogeography-based optimization with re-initialization and local search

Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm

A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization

Beyond Standard Particle Swarm Optimisation

MO-TRIBES, an adaptive multiobjective particle swarm optimization algorithm