Models for iterative global optimization
Models for iterative global optimization
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Selected Papers from AISB Workshop on Evolutionary Computing
Analyzing the role of "smart" start points in coarse search-greedy search
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
An Analysis of Locust Swarms on Large Scale Global Optimization Problems
ACAL '09 Proceedings of the 4th Australian Conference on Artificial Life: Borrowing from Biology
Optimization in Fractal and Fractured Landscapes Using Locust Swarms
ACAL '09 Proceedings of the 4th Australian Conference on Artificial Life: Borrowing from Biology
Particle swarm optimization with resets: improving the balance between exploration and exploitation
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Selection strategies for initial positions and initial velocities in multi-optima particle swarms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
An analysis of sub-swarms in multi-swarm systems
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
A simple strategy to maintain diversity and reduce crowding in particle swarm optimization
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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Locust Swarms are a new multi-optima search technique explicitly designed for non-globally convex search spaces. They use "smart" start points to scout for promising new areas of the search space before using particle swarms and a greedy local search technique (e.g. gradient descent) to find a local optimum. These scouts start a minimum distance away from the previous optimum, and this gap is an important part of achieving a non-convergent search trajectory. Equally, the search for "smart" start points centers around the previous local optimum, and this provides the basis for also having a non-random search trajectory. Experiments on a 30- dimensional rotated Schwefel function demonstrate that the ability of Locust Swarms to successfully balance these two search characteristics is an important factor in its ability to effectively explore this non-globally convex search space.