Strength in Numbers: The Labour Government and the Size of the Polaris Force

Previous historical accounts have simplified the Labour government's decision to commission a four-boat strategic nuclear deterrent, or ‘Polaris force’, in early 1965. Utilising previously classified sources, this article shows that a number of key strategic concerns led the decision-making process. Nevertheless, broader economic and diplomatic considerations were also important in determining the size of the Polaris force. A tremendous balance of payments deficit and ongoing debates regarding levels of conventional and nuclear involvement in NATO certainly influenced the government's final decision. Moreover, competing strategic and economic interests resulted in continued debate throughout the decision-making process.     

Book reviews

Soviet Nuclear Strategy from Stalin to Gorbachev: a Revolution in Soviet Military and Political Thinking. By Honore M. Catudal. Mansell, London (1988), ISBN 0–7201–2000–4, £25.00

The Military: More Than Just a Job? Edited by Charles C. Moskos and Frank R. Wood. Pergamon‐Brassey's, London (1988), ISBN 0–08–034321‐X, £17.00 ($30.00)

Douglas Haig, 1861–1928. By Gerard J. de Groot. Unwin Hyman, London (1988), ISBN 004 4401922, £20.00

Defence Policy Making. A Comparative Analysis. Edited by G. M. Dillon. Leicester University Press, Leicester (1988), ISBN 0–715–1268–5, £10.95

The Future of U.K. Air Power. Edited by P. Sabin. Brassey's, London (1988), ISBN 0–08–035825‐X (hardcover), 0–08–036256–7 (flexicover), hardcover £18.95 ($34.00), flexicover £9.95 ($17.95)     

Using multiple searchers in constrained-path,moving-target search problems

The search theory open literature has paid little, if any, attention to the multiple-searcher, moving-target search problem. We develop an optimal branch-and-bound procedure and six heuristics for solving constrained-path problems with multiple searchers. Our optimal procedure outperforms existing approaches when used with only a single searcher. For more than one searcher, the time needed to guarantee an optimal solution is prohibitive. Our heuristics represent a wide variety of approaches: One solves partial problems optimally, two use paths based on maximizing the expected number of detections, two are genetic algorithm implementations, and one is local search with random restarts. A heuristic based on the expected number of detections obtains solutions within 2% of the best known for each one-, two-, and three-searcher test problem considered. For one- and two-searcher problems, the same heuristic's solution time is less than that of other heuristics. For three-searcher problems, a genetic algorithm implementation obtains the best-known solution in as little as 20% of other heuristic solution times. © 1996 John Wiley & Sons, Inc.