No abstract

No abstract

In order to solve more complex application problems, parallel particle swarm optimization (PPSO) was proposed in recent years and attracted more and more interests from researchers. Multipopulation PPSO is one of important models, it needs less processors and its communication cost is lower than fine-grained PPSO. When to design multipopulation PPSO, several additional parameters for parallelization should be considered first. They are topological structure, migration strategy, migration interval, migration rate, etc. Among them, migration strategy is an important parameter that heavily affects the performance of multipopulation PPSO. However, there is few work on this important parameter. In this paper, we proposed 8 migration strategies for multipopulation PPSO. Compared with most used One-To-Migrate strategies on 36 commonly used test functions, we found that both strategies BW and BWM are more efficient for high dimensionality problem, while on low dimensionality functions One-To-Migrate strategies are more effective. And what is better than our expected is that the strategy RR is most effective on some functions.

The maximum cut (MAX-CUT) problem is to find a bipartition of the vertices in a given graph such that the number of edges with ends in different sets reaches the largest. Though, several experimental investigations have shown that evolutionary algorithms (EAs) are efficient for this NP-complete problem, there is little theoretical work about EAs on the problem. In this paper, we theoretically investigate the performance of EAs on the MAX-CUT problem. We find that both the (1+1) EA and the (1+1) EA*, two simple EAs, efficiently achieve approximation solutions of (m/2)+(1/4)s(G) and (m/2)+(1/2)(√{8m+1}-1), where m and s(G) are respectively the number of edges and the number of odd degree vertices in the input graph. We also reveal that for a given integer k the (1+1) EA* finds a cut of size at least k in expected runtime O(nm+1/δ(4k)) and a cut of size at least (m/2)+k in expected runtime O(n(2)m+1/δ((64/3)k(2))), where δ is a constant mutation probability and n is the number of vertices in the input graph. Finally, we show that the (1+1) EA and the (1+1) EA* are better than some local search algorithms in one instance, and we also show that these two simple EAs may not be efficient in another instance.

No abstract